Evaluation and comparison of satellite-based rainfall products in Burkina Faso,West Africa

The performance of seven operational high-resolution satellite-based rainfall products – Africa Rainfall Estimate Climatology (ARC 2.0), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation (RFE 2.0), Tropical Applications of Meteorology using SATellite (TAMSAT), African Rainfall Climatology and Time-series (TARCAT), and Tropical Rainfall Measuring Mission (TRMM) daily and monthly estimates – was investigated for Burkina Faso. These were compared to ground data for 2001–2014 on a point-to-pixel basis at daily to annual time steps. Continuous statistics was used to assess their performance in estimating and reproducing rainfall amounts, and categorical statistics to evaluate rain detection capabilities. The north–south gradient of rainfall was captured by all products, which generally detected heavy rainfall events, but showed low correlation for rainfall amounts. At daily scale they performed poorly. As the time step increased, the performance improved. All (except TARCAT) provided excellent scores for Bias and Nash–Sutcliffe Efficiency coefficients, and overestimated rainfall amounts at the annual scale. RFE performed the best, whereas TARCAT was the weakest. Choice of product depends on the specific application: ARC, RFE, and TARCAT for drought monitoring, and PERSIANN, CHIRPS, and TRMM daily for flood monitoring in Burkina Faso.     

Comprehensive evaluation of 3-hourly TRMM and half-hourly GPM-IMERG satellite precipitation products

Accurate estimation of precipitation is crucial for crop yield assessment, flood and drought monitoring, and water structures management. Precipitation is subject to both temporal and spatial variability. While recording rain gauges support temporal resolution, they measure point rainfall and require dense network and application of interpolation techniques to provide spatial dimension. On the other hand, remote-sensing products cover regional and global spatial scales. Building upon the Tropical Rainfall Measuring Mission (TRMM) heritage, the Global Precipitation Measurement (GPM) mission is an international net of satellites that present the next-generation global observations of rain and snow at a spatial resolution of 0.1° × 0.1° with a half-hour temporal resolution. In this study, March–December 2014 3-hourly TRMM data (3B42V7) and half-hourly Integrated Multi-satellite Retrievals for GPM (IMERG) data are compared with the 3-hourly rain gauges data in Khorasan Razavi province, located in northwest of Iran. Coefficient of determination (R²), Bias, MBias, RBias, mean absolute error (MAE), root mean square error (RMSE) as well as probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics were measured for validation purposes. The result showed that correlation between IMERG data and rain gauge rainfall data is higher than those of 3B42V7 data. In addition, the values of MBias, Bias, and RBias confirmed that both of 3B42V7 and IMERG underestimated rainfall over the study area, whereas MBias of IMERG was higher than 3B42V7. Furthermore, MAE and RMSE values of 3B42V7 and IMERG were similar while IMERG evaluation turned out a better correlation coefficient (r) and POD than 3B42V7. This study showed that IMERG generally had reasonable agreement with the gauge data.     

Investigating the performance of satellite and reanalysis rainfall products at monthly timescales across different rainfall regimes of Ethiopia

Continuous availability of a variety of satellite and reanalysis rainfall products have triggered the use of such products as an alternate source of rainfall data in sparsely gauge networked areas. However, before utilizing them a detailed validation of these datasets are essential to have some level of guarantee. In many parts of Africa in general and most parts of Ethiopia particularly in the lowland areas, gauge stations are very sparse and unevenly distributed. In addition, due to the nature of complex topography and geographical location, Ethiopian rainfall shows high variability both temporally and spatially. In view of the above, the present study is aimed at statistically evaluating such rainfall products across different rainfall regimes (regions with different rainfall characteristics as defined by National Meteorological Agency (NMA) of Ethiopia). In the current study, five satellite and two reanalysis rainfall products such as African Rainfall Climatology version 2 (ARC2), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT), Tropical Rainfall Measuring Mission-3B43 version 7 (TRMM 3B43v7), Climate Prediction Center Morphing Technique (CMORPH), Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2), the Climate Forecast System Reanalysis (CFSR) and the European Center for Medium Range Weather Forecast Reanalysis (ERA-Interim) are considered based on their spatial coverage, spatial resolution, temporal resolution, latency period and length of data records. Evaluation is done at monthly and seasonal time scales against the observed gauge rainfall data provided by the National Meteorological Agency of Ethiopia across entire Ethiopia in two different manners, first by considering the entire country as one homogeneous unit and secondly in a distributed manner across the three rainfall regimes of Ethiopia. The obtained results show that: (i) CHIRPSv2 and TRMM 3B43v7 show better performance during June to September (the main rainfall season) and during February to May (the smaller rainfall season) in regimes 1 and 2. (ii) In regime 3 these products show good performance from October to November (smaller rainy season of this regime) and March to May (main rainy season of this regime); (iii).CMORPH, TAMSAT and ARC2 show moderate performance in all three regimes; (iv) CFSR and ERA-Interim exhibit poor performance in all rainfall regimes. Overall, the detailed analysis of statistical evaluation results of the rainfall products at monthly timescale shows that CHIRPSv2 performs comparatively better than the other tested rainfall products across all rainfall regimes. However, the best performance of CHIRPSv2 is obtained in regime 2 followed by regime 1 and regime 3.     

Multi-scale evaluation of six high-resolution satellite monthly rainfall estimates over a humid region in China with dense rain gauges

This study evaluates and compares the performance of six high-resolution monthly satellite rainfall estimates (SREs), which include TRMM 3B43V6, TRMM 3B42RTV6, CMORPH, GSMaP MWR+, GSMaP MVK+, and PERSIANN, with dense ground rain gauges located in Ganjiang River Basin. The performance was evaluated at multiple spatial scales: the 0.25° × 0.25° grid, sub-catchment, and the whole basin. It was observed that 3B43V6 generally performed well and was able to capture the ground benchmark rainfall with slight overestimation, whereas all of the other SREs suffered large underestimation in the study area. Among the five pure satellite-derived products, 3B42RTV6 and CMORPH performed better, whereas PERSIANN performed the worst. All of the SREs except 3B43V6 showed a strong seasonal signature with much better performance in the wet season than in the dry season. The results also indicate that SREs performed better in the southeast and central regions, whereas poor performance was observed in the western mountains and in the northern plains. Furthermore, the spatial patterns of SREs errors are influenced mainly by the local terrain. The performance of SREs improved when the spatial scale was increased, whereas the performance reduced when the temporal scale was increased from month to year.     

Evaluating three satellite-based precipitation products of different spatial resolutions in Shanghai based on upscaling of rain gauge

Gridded precipitation products have been widely used in scientific literatures, e.g., weather forecasting, hydrological process modelling and disaster simulating. However, it is necessary to evaluate their accuracies before using them for practical purposes. In this study, three mainstream different-spatial-scale daily gridded precipitation data (GPD), including [Global Satellite Mapping of Precipitation–gauge adjusted (GSMAP_Gauge), TRMM 3B42 version-7 (TRMM 3B42V7) and Global Precipitation Climatology Project version 1.2 (GPCP-V1.2)] were systematically evaluated against local rain gauges for their ability to detect precipitation characteristics of Shanghai in 2008 based on upscaling of rain gauge. In general, the gridded precipitation products overestimate precipitation compared to upscaled ground observations (UGOs). However, all of the products have an obvious underestimation on extreme precipitation (>50 mm day^?1). In contrast, the results show that GSMaP_Gauge has the highest correlation coefficient, TRMM 3B42V7 has the lowest RMSE, and Probability of Detection (POD) in GPCP-V1.2 is better than that in TRMM 3B42V7 and GSMaP_Gauge. As the spatial resolution decreases, the frequency and amount of extreme precipitation events decrease regardless of detection from UGOs and GPD. We also find that those heavy precipitation events detected from TRMM 3B42V7 at 0.25° and 1.0° are significantly underestimated. Overall, we believe that it is necessary to evaluate gridded precipitation products at the urban scale based on upscaling of rain gauge.     

Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran

The objective of this research is to evaluate daily rain rates derived from three widely used high-resolution satellite precipitation products (PERSIANN, TMPA-3B42V7, and TMPA-3B42RT) using rain gauge observations over the entire country of Iran. Evaluations are implemented for 47 comprehensive daily rainfall events during the winter and spring seasons from 2003 to 2006. These events are selected because each encompasses more than 50% of the country’s area. In this study, daily rainfall observations derived from 1180 rain gauges distributed throughout the country are employed as reference surface data. Six statistical indices: bias, multiplicative bias (MBias), relative bias (RBias), mean absolute error (MAE), root mean square error (RMSE), and linear correlation coefficient (CC), as well as a contingency table are applied to evaluate the satellite rainfall estimates qualitatively. The spatially averaged results over the entire country indicate that 3B42V7, with an average bias value of –1.47 mmd^?1, RBias of –13.6%, MAE of 4.5 mmd^?1, RMSE of 6.5 mmd^?1, and CC of 0.61, leads to better estimates of daily precipitation than those of PERSIANN and 3B42RT. Furthermore, PERSIANN with an average MBias value of 0.56 tends to underestimate precipitation, while 3B42V7 and 3B42RT with average MBias values of 0.86 and 1.02, respectively, demonstrate a reasonable agreement in regard to rainfall estimations with the rain gauge data. With respect to the categorical verification technique implemented in this study, PERSIANN exhibits better results associated with the probability of detection of rainfall events; however, its false alarm ratio is worse than that of 3B42V7 and 3B42RT.     

Multitemporal comparative analysis of TRMM-3B42 satellite-estimated rainfall with surface gauge data at basin scales: daily,decadal and monthly evaluations

In order to examine the reliability and applicability of Tropical Rainfall Measuring Mission (TRMM) and Other Satellites Precipitation Product (3B42) Version 6 (TRMM-3B42) at basin scales, satellite rainfall estimates were compared with geostatistically interpolated reference data from 12 rain gauge stations for three consecutive years: 2005, 2006 and 2007. Gauge–TRMM-3B42 statistical properties for daily, decadal and monthly multitemporal precipitations were compared using the following cross-validation continuous statistical measures: mean bias error (MBE), root mean square difference (RMSD), mean absolute difference (MAD) and coefficient of determination (r ²) metrics. The averaged spatial–temporal comparisons showed that the TRMM-3B42 rainfall estimates were much closer to the geostatistically interpolated gauge data, with minimal biases of??0.40 mm day^?1,??1.78 mm decad^?1 and??6.72 mm month^?1 being observed in 2006. In the same year, the gauge and TRMM-3B42 rainfall estimates marginally correlated better than in 2005 and 2007, with the daily, decadal and monthly coefficients of determination being 82.2%, 93.9% and 96.5%, respectively. The results showed that the correlations between the gauge-derived precipitation and the TRMM-3B42-derived precipitation increased with increasing temporal intervals for all three considered years. Quantitatively, the TRMM-3B42 observations slightly overestimated the precipitations during the wet seasons and underestimated the observed rainfall during the dry seasons. The results of the study show that the estimates from TRMM-3B42 precipitation retrievals can effectively be applied in the interpolation of missing gauge data, and in the verification of precipitation uncertainties at the basin scales with minor adjustments, depending on the timescales considered.     

An evaluation of high-resolution multisatellite rainfall products over the Indian monsoon region

To date, more than half a dozen merged rainfall data sets are available to the research community. These data sets use different approaches for rainfall retrieval and combine different satellites or/and ground-based rainfall observations. However, these data sets appear to differ among themselves and deviate from in situ observations at regional and seasonal scales. Hence, it is becoming difficult to choose a suitable data set from these products for regional rainfall analyses. In the present study, four independently developed multisatellite high-resolution precipitation products (HRPPs), namely Climate Prediction Center Morphing (CMORPH) version 1.0, Naval Research Laboratory (NRL)–blended, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)–3B42 version 7 are compared with quality-controlled gridded rain gauge data over India developed by the India Meteorological Department (IMD). A preliminary analysis is carried out for a 6 year period from 2004 to 2009 at daily scale for the summer monsoon season of June to September. Comparison of all-India seasonal (June to September) mean rainfall with rain gauge data shows a considerable underestimation by all HRPPs, although the underestimation is comparatively less for TMPA. Moreover, all the HRPPs are able to capture the important characteristic features of the summer monsoon rainfall such as intra-seasonal (active/break spells) and inter-annual (excess/deficient) variabilities reasonably well. Regional differences between observed rainfall and the HRPPs are also analysed. Results suggest that TMPA is comparatively closer to the ground-truth, possibly due to the incorporation of rain gauge observations. Furthermore, all the HRPPs show high probability of detection, low false alarm ratio, and high threat score in detection of rainfall events over most parts of India. It is also observed that all these HRPPs have certain issues in rainfall detection over the rain-shadow region of southeast peninsular India, semi-arid northwest parts of India, and hilly northern parts. Hence, results of the 6 year analysis over a region with a dense network of surface observations of rainfall suggest that the TMPA merged rainfall product is better than the other HRPPs due to (1) lower underestimation of rainfall, (2) higher correlation and lower root-mean-square error (RMSE), and (3) better performance over the west coast. Therefore, TMPA can be used with confidence as compared to other HRPPs for monsoon studies, particularly over the Indian land region with a considerable rain gauge network. This study also clarifies the fact that the merged satellite rainfall products with sufficient ground-truths can be the ideal product for monsoon and hydrological studies.     

Validation of high‐resolution satellite rainfall products over complex terrain

High‐resolution satellite rainfall products, at daily accumulation and 0.25° spatial resolution, are evaluated using station networks located over two different parts of Africa. The first site is located over Ethiopia with a very complex terrain. The second site, located over Zimbabwe, has a less rugged topography. The evaluated satellite rainfall products are the NOAA‐CPC African rainfall estimation algorithm (RFE), TRMM‐3B42, the CPC morphing technique (CMORPH), PERSIANN, and the Naval Research Laboratory's blended product. These products perform reasonably well over both regions in detecting the occurrence of rainfall. However, performances are poor in estimating the amount of rainfall in each pixel. The correlation coefficients are low and random errors high. The performance was better over Zimbabwe as compared with Ethiopia. Comparing the different products, CMORPH and TRMM‐3B42 showed a better performance over Ethiopia, while RFE, CMORPH, and TRMM‐3B42 preformed relatively better over Zimbabwe.     

Evaluation of gridded precipitation datasets for the Kabul Basin,Afghanistan

The availability of high-resolution gridded precipitation products based on satellite estimations and/or interpolated observations provides a good opportunity to monitor precipitation over large and remote areas of poorly gauged basins. Precipitation is the key input in hydrological modelling for the assessment and management of water resources. However, it is necessary to validate the accuracy of these precipitation products before their application towards the planning and management of the water resources. The objective of this study, therefore, was to validate gridded precipitation time series data in Climate Prediction Centre – Rainfall Estimates (CPC-RFE 2.0), Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK V5), Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA V7), and Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources, Monsoon Asia (APHRODITE MA V1101) using the data from recently established rain gauges over the Kabul basin in Afghanistan from 2004 to 2007 (the common period of observations). These products were evaluated at different spatial and temporal resolutions (daily, monthly, and annual). The validation approach used here includes continuous (mean absolute error, root mean square error, correlation coefficient (r), and multiplicative bias) and categorical (probability of detection and false alarm ratio) verification statistics. Furthermore, the spatial performance was evaluated by mapping the data and analysing the distribution of precipitation as a function of elevation. The results of continuous and categorical verification statistics suggest that the APHRODITE MA V1101 dataset performs better than other gridded datasets for the study basin. The estimates from four tested products showed a relatively good detection of the amount and distribution of precipitation in the Kabul basin.     

Downscaling CHIRPS precipitation data: an artificial neural network modelling approach

The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) is a high-resolution climatic database of precipitation embracing monthly precipitation climatology, quasi-global geostationary thermal infrared satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 product, atmospheric model rainfall fields from National Oceanic and Atmospheric Administration – Climate Forecast System (NOAA CFS), and precipitation observations from various sources. The key difference with all other existing precipitation databases is the high-resolution of the available data, since the inherent 0.05° resolution is a rather unique threshold. Monthly data for the period from January 1999 to December 2012 were processed in the present research. The main aim of this article is to propose a novel downscaling method in order to attain high resolution (1 km × 1 km) precipitation datasets, by correlating the CHIRPS dataset with altitude information and the normalized difference vegetation index from satellite images at 1 km × 1 km, utilizing artificial neural network models. The final result was validated with precipitation measurements from the rain gauge network of the Cyprus Department of Meteorology.     

Assessment and correction of three satellite rainfall estimate products for improving flood prevention in French Guiana

The French Guiana (80 000 km²) is highly vulnerable to flooding during the rainy season but the hydrological prevision is limited because the region cannot be cover by a dense network of rain gauges. Meteorological satellites could be an alternative for the measurement of precipitation. The objective of this paper was to evaluate and improve the accuracy of daily satellite rainfall estimates (SRE) throughout the French Guiana between April 2015 and March 2016. Validation data were composed by 70 rain gauges managed by France and Suriname. Three satellite-based rainfall estimates have been tested: TRMM-TMPA 3B42 (Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis) V7, IMERG (Integrated Multi-satellitE Retrievals) for GPM (Global Precipitation Measurement) and STAR Satellite rainfall estimates Hydro-Estimator (HE). Better SRE were obtained by GPM with a clearly higher probability of detection of rainy days (>70%). During the rainy season, biases remained important and SRE appeared inaccurate for the monitoring and forecasting of floods. Biases correction methods were applied, and the additive correction methods by interpolation of biases (ADD_IDW) obtained the better performance (absolute biases <8 mm day^?1; RMSE <12 mm day^?1) for each satellite products. This simple method proved to be very effective to reduce biases close to 0 throughout the year. After ADD_IDW correction, performance levels of TRMM, GPM, and HE products were relatively close and these three satellite products could be implemented into cascade chains in operational framework ensuring the provision of corrected SRE in real time and thus guarantee a reinforced hydrological monitoring in French Guiana.     

Evaluation of multi-satellite precipitation products in Xinjiang,China

ABSTRACT

Satellite precipitation retrieval is a critical approach to understanding the spatial distribution of precipitation in Xinjiang, an arid area located in Northwest China, where weather stations are sparsely distributed. However, satellite precipitation retrieval in arid areas is a challenging task. The goal of this study is to evaluate the estimates of four satellite precipitation products, namely, the Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis 3B42 (3B42), and Climate Prediction Center Morphing Technique (CMORPH), on half-hourly, hourly, 3-hourly, and daily scales based on rain gauge data. The findings of this study are as follows. (1) The four products generally display a declining trend from northwest to southeast. IMERG exhibits a higher accuracy than CMORPH for all indexes at the half-hourly scale, while GSMaP performs better than other products based on most indexes at hourly and daily scales. (2) In three sub-regions, i.e. Southern Xinjiang (SX), Northern Xinjiang (NX) and the Tianshan Mountains (Tianshan), these products exhibit significant regional characteristics. The precipitation in SX, where rainfall observations are scarce, is overestimated by all products; in contrast, all products underestimate precipitation in Tianshan in NX, except for the underestimation by 3B42, precipitation was overestimated by the studied products. (3) All satellite products performed better in the warm season than in the cold season at each time scale. During the warm season, apart from the apparent overestimation by CMORPH, the relative bias values of the other products are all within ±10%. During the cold season, all products underestimate precipitation mainly composed of snowfall, especially 3B42, which yields the most underestimated values. (4) IMERG performs well in the retrieval of the distribution of the probability density function (PDF) of the occurrence (PDFc) of gauge observations, especially at low precipitation intensities, and the difference between the estimated and observed precipitation volumes at the hourly scale is the smallest. However, GSMaP performed better at the daily scale according to the PDF for the volume of precipitation (PDFv). This study is the first to evaluate IMERG and CMORPH products at the half-hourly scale and is one of the few sub-daily evaluations of various satellite precipitation products in arid areas of China. Thus, our results provide a significant reference for the satellite retrieval of precipitation in arid areas.     

遥感降水数据精度检验策略及检验方法综述

卫星遥感反演降水技术为获取全球降水信息提供了途径。遥感降水数据不可避免地存在误差,精度检验是必不可缺的。首先回顾了遥感降水数据全球检验项目;总结检验策略,分为降水事件检验、中小区域尺度检验和大区域尺度检验;然后总结检验方法,包括基于地面观测数据检验方法和基于降水产品检验方法;最后介绍遥感降水数据检验常用流程,包括获取参照数据、检验区域选择和数据对比。遥感降水产品精度检验框架主要存在3个方面的问题,全球遥感降水产品检验策略不完善、参照数据缺乏统一标准和检验指标不统一。未来发展方向可能有完善检验策略、统一参照数据及检验方法。     

An evaluative study of TRMM precipitation estimates over multi-day scales in a semi-arid region,Iran

Data from the Tropical Rainfall Measuring Mission (TRMM) rainfall estimations have been evaluated at different time scales in the previous research, in particular, sub-daily, monthly, seasonally and annually. However, in arid and semi-arid regions water balance may be reached several days after a rainfall event. Hence, it becomes of crucial importance to investigate sub-monthly time periods (i.e. multi-day periods). For this reason, TRMM precipitation data version 3B42 (3B42) were evaluated and calibrated for 1, 2, 3, 5, 7, 10, 15, 20 days and monthly time scales using rain gauges data in Fars province, Islamic Republic of Iran, 1 January 2000 to 31 December 2014. Pearson’s correlation coefficient (r), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Bias (MB), Prediction of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI) were used for the purpose of evaluation. The results showed that with a logarithmic trend, r, NRMSE, and FAR values decreased while RMSE, CSI, and POD values increased with increasing time scales. Moreover, the spatial average MB was almost constant for various time scales, although the percentage of grid cells with over-estimated rainfall increased from 1 day to 1 month. By fitting logarithmic functions over the values of r, RMSE, NRMSE, POD, FAR, and CSI at 1, 10 days, and monthly time scales, the corresponding values of these measures were predicted for other time scales with the relative error (NRMSE) of less than 0.1, which indicates the accurate performance of these functions. Through linear regression analysis, the slope (M) and interception (B) of the equations for calibrating 3B42 precipitation estimates at various time scales were obtained. Furthermore, the results showed that the obtained values of M and B in 1, 10 days, and monthly time scales can be estimated with a high accuracy at 2, 3, 5, 7, 15, and 20 days.     

Evaluation and assimilation of various satellite-derived rainfall products over India

Accurate prediction of rainfall from the numerical weather prediction model is one of the major objectives over tropical regions. In this study, four different satellite-derived rainfall products (viz. merged-rainfall product from TRMM (Tropical Rainfall Measuring Mission) 3B42 and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)), and Indian meteorological satellite INSAT-3D retrieved HEM (Hydro-Estimator Method) and IMSRA (INSAT Multi-Spectral Rainfall Algorithm) rainfall) are assimilated in the Weather Research and Forecasting (WRF) model using variational method. Before assimilation of satellite retrieved rainfall product in the WRF model, selected rainfall products are compared with ground rainfall from India Meteorological Department during Indian summer monsoon (June–September) 2015. Preliminary validation results show root-mean-square-difference (mean difference) of 18.1 (2.1), 21.3 (2.1), 15.4 (?0.72), and 14.4 (0.5) mm day^?1 in IMSRA, HEM, IMERG, and TRMM 3B42 rainfall, respectively. Further, the four-dimensional variational data assimilation method is used daily to assimilate selected rainfall products in the WRF model during the entire month of August 2015. Results suggest that assimilation of satellite rainfall improved the WRF model analyses and subsequent temperature and moisture forecasts. Moreover, rainfall prediction is also improved with the maximum positive impact from TRMM rainfall assimilation followed by IMERG rainfall assimilation. Similar nature of improvements is also seen in rainfall prediction when INSAT-3D retrieved rainfall products (HEM and IMSRA) are used for assimilation.     

Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period

Rainfall is one of the key drivers of the global hydrological cycle and has large socio-economic impacts. Tropical rainfall accounts for two-thirds of the global rainfall and is primarily associated with the monsoon. Multi-satellite rainfall products provide rainfall with high temporal and spatial resolutions; however, they exhibit regional and seasonal biases. Evaluation of these products against ground-based observations can improve the accuracy of the estimated rainfall. With the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high-resolution multi-satellite precipitation products namely; Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are released. In the present study the spatial and temporal structures of rainfall in near real time and research versions of IMERG-V4 (near real-time (NRT) & Final (FNL)), GSMaP-V6 (NRT & moving vector with Kalman filter (MVK)), INSAT3D (Indian National Satellite System (INSAT) Multispectral Rainfall Algorithm (IMR) & Hydro-Estimator method (HEM)) and Indian Meteorological Department (IMD) – National Centre for Medium Range Weather Forecasting (NCMRWF) Merged product have been evaluated against gridded gauge-based IMD rainfall data on daily, monthly and seasonal scales. All the datasets show noticeable bias in producing rainfall over orographic regions (i.e. Western Ghats and foothills of Himalayas) and North-East India, though there exists significant difference among the satellite measurements. Different skill scores are computed for GSMaP, IMERG and INSAT3D data products to evaluate the performance of these satellite estimates. However in terms of biases IMD-NCMRWF Merged, GSMaP (NRT & MVK) and IMERG (NRT & FNL) underestimates rainfall (about 11%, 17%, 23%, 18% and 3%, respectively) and INSAT3D (IMR & HEM) overestimates (about 49% and 33%, respectively), for the India region as a whole. In a similar way, HEM product shows 15% better performance than IMR product in INSAT3D category. However, both NRT and MVK products of GSMaP show similar variations compared to observe rainfall. Overall IMD–NCMRWF merged and IMERG-FNL data products show better agreement with the gauge-based IMD data compared to GSMaP. The GPM-based products (IMERG and GSMaP) estimate rainfall much better than INSAT3D estimation.     

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

In 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.     

TRMM rainfall correction over the Andean Plateau using wavelet multi-resolution analysis

Quantifying rainfall from remotely sensed data is crucial for regions where meteorological stations are scarce. This might be one of the only options for analysing rainfall patterns at different temporal and spatial scales in data-scarce environments, particularly in developing countries. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall estimation over the tropics. Rainfall estimates from the TRMM satellite exhibit inaccuracies over topographically complex regions, thus warranting suitable corrections. Multi-resolution analysis (MRA) was applied to improve TRMM 3B42 daily rainfall estimation at 19 meteorological stations located over the Andean Plateau. The detailed signal from each meteorological station was added to the trend signal of each TRMM data cell. Comparing raw and corrected TRMM with gauged rainfall revealed that wavelet-based correction of TRMM 3B42 on average improved several metrics: entropy difference (15.45?1.32), determination coefficient (0.07?0.92), bias (0.68?1.01) and relative mean absolute error (RMAE, 0.86?0.59). The entropy difference of corrected TRMM and gauged rainfall was less than 5%, even when TRMM correction was performed with noise from a station located up to 565 km away from the TRMM cell. This entropy difference corresponded to an average bias of less than 10% in the rainfall estimation.