Satellite-based Rainfall Estimates Research Articles

Validation of satellite-based rainfall in Kalahari

Water resources management in arid and semi-arid areas is hampered by insufficient rainfall data, typically obtained from sparsely distributed rain gauges. Satellite-based rainfall estimates (SREs) are alternative sources of such data in these areas. In this study, daily rainfall estimates from FEWS-RFE∼11 km, TRMM-3B42∼27 km, CMOPRH∼27 km and CMORPH∼8 km were evaluated against nine, daily rain gauge records in Central Kalahari Basin (CKB), over a five-year period, 01/01/2001-31/12/2005. The aims were to evaluate the daily rainfall detection capabilities of the four SRE algorithms, analyze the spatio-temporal variability of rainfall in the CKB and perform bias-correction of the four SREs. Evaluation methods included scatter plot analysis, descriptive statistics, categorical statistics and bias decomposition. The spatio-temporal variability of rainfall, was assessed using the SREs' mean annual rainfall, standard deviation, coefficient of variation and spatial correlation functions. Bias correction of the four SREs was conducted using a Time-Varying Space-Fixed bias-correction scheme. The results underlined the importance of validating daily SREs, as they had different rainfall detection capabilities in the CKB. The FEWS-RFE∼11 km performed best, providing better results of descriptive and categorical statistics than the other three SREs, although bias decomposition showed that all SREs underestimated rainfall. The analysis showed that the most reliable SREs performance analysis indicator were the frequency of “miss” rainfall events and the “miss-bias”, as they directly indicated SREs' sensitivity and bias of rainfall detection, respectively. The Time Varying and Space Fixed (TVSF) bias-correction scheme, improved some error measures but resulted in the reduction of the spatial correlation distance, thus increased, already high, spatial rainfall variability of all the four SREs. This study highlighted SREs as valuable source of daily rainfall data providing good spatio-temporal data coverage especially suitable for areas with limited rain gauges, such as the CKB, but also emphasized SREs' drawbacks, creating avenue for follow up research.

Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel

Abstract. Climate change in drylands has caused alterations in the seasonal distribution of rainfall including increased heavy-rainfall events, longer dry spells, and a shifted timing of the wet season. Yet the aboveground net primary productivity (ANPP) in drylands is usually explained by annual-rainfall sums, disregarding the influence of the seasonal distribution of rainfall. This study tested the importance of rainfall metrics in the wet season (onset and cessation of the wet season, number of rainy days, rainfall intensity, number of consecutive dry days, and heavy-rainfall events) for growing season ANPP. We focused on the Sahel and northern Sudanian region (100–800 mm yr−1) and applied daily satellite-based rainfall estimates (CHIRPS v2.0) and growing-season-integrated normalized difference vegetation index (NDVI; MODIS) as a proxy for ANPP over the study period: 2001–2015. Growing season ANPP in the arid zone (100–300 mm yr−1) was found to be rather insensitive to variations in the seasonal-rainfall metrics, whereas vegetation in the semi-arid zone (300–700 mm yr−1) was significantly impacted by most metrics, especially by the number of rainy days and timing (onset and cessation) of the wet season. We analysed critical breakpoints for all metrics to test if vegetation response to changes in a given rainfall metric surpasses a threshold beyond which vegetation functioning is significantly altered. It was shown that growing season ANPP was particularly negatively impacted after >14 consecutive dry days and that a rainfall intensity of ∼13 mm day−1 was detected for optimum growing season ANPP. We conclude that the number of rainy days and the timing of the wet season are seasonal-rainfall metrics that are decisive for favourable vegetation growth in the semi-arid Sahel and need to be considered when modelling primary productivity from rainfall in the drylands of the Sahel and elsewhere.

Towards combining GPM and MFG observations to monitor near real time heavy precipitation at fine scale over India and nearby oceanic regions

This is the first attempt to merge highly accurate precipitation estimates from Global Precipitation Measurement (GPM) with gap free satellite observations from Meteosat to develop a regional rainfall monitoring algorithm to estimate heavy rainfall over India and nearby oceanic regions. Rainfall signature is derived from Meteosat observations and is co-located against rainfall from GPM to establish a relationship between rainfall and signature for various rainy seasons. This relationship can be used to monitor rainfall over India and nearby oceanic regions. Performance of this technique was tested by applying it to monitor heavy precipitation over India. It is reported that our algorithm is able to detect heavy rainfall. It is also reported that present algorithm overestimates rainfall areal spread as compared to rain gauge based rainfall product. This deficiency may arise from various factors including uncertainty caused by use of different sensors from different platforms (difference in viewing geometry from MFG and GPM), poor relationship between warm rain (light rain) and IR brightness temperature, and weak characterization of orographic rain from IR signature. We validated hourly rainfall estimated from the present approach with independent observations from GPM. We also validated daily rainfall from this approach with rain gauge based product from India Meteorological Department (IMD). Present technique shows a Correlation Coefficient (CC) of 0.76, a bias of −2.72mm, a Root Mean Square Error (RMSE) of 10.82mm, Probability of Detection (POD) of 0.74, False Alarm Ratio (FAR) of 0.34 and a Skill score of 0.36 with daily rainfall from rain gauge based product of IMD at 0.25° resolution. However, FAR reduces to 0.24 for heavy rainfall events. Validation results with rain gauge observations reveal that present technique outperforms available satellite based rainfall estimates for monitoring heavy rainfall over Indian region.

Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka

The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited.

An assessment of the performance of global rainfall estimates without ground-based observations

Abstract. Satellite-based rainfall estimates over land have great potential for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of triple collocation (TC) to characterize uncertainties associated with rainfall estimates by using three collocated rainfall products. However, TC requires the simultaneous availability of three products with mutually uncorrelated errors, a requirement which is difficult to satisfy with current global precipitation data sets. In this study, a recently developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the-art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high-quality ground rainfall product over the contiguous United States (CONUS), showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (vs. an unknown truth) of the aforementioned products without using a ground benchmark data set. The analysis is carried out during the period 2007–2012 using daily rainfall accumulation products obtained at 1° × 1° spatial resolution. Results convey the relatively high performance of the satellite rainfall estimates in eastern North and South America, southern Africa, southern and eastern Asia, eastern Australia, and southern Europe, as well as complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the Northern Hemisphere and the second providing very good performance in the Southern Hemisphere. The methodology presented in this study can be used to identify the best rainfall product for hydrologic models with sparsely gauged areas and provide the basis for an optimal integration among different rainfall products.

Satellite-based rainfall estimation and discharge measurement of Middle Indus River, Pakistan

The impacts of floods and droughts are intensified by climate change, lack of preparedness, and coordination. The average rainfall in study area is ranging from 200 to 400 mm per year. Rain gauge generally provides very accurate measurement of point rain rates and the amounts of rainfall but due to scarcity of the gauge locations provides very general information of the area on regional scale. Recognizing these practical limitations, it is essential to use remote sensing techniques for measuring the quantity of rainfall in the Middle Indus. In this research, Tropical Rainfall Measuring Mission (TRMM) estimation can be used as a proxy for the magnitude of rainfall estimates from classical methods (rain gauge), quantity, and its spatial distribution for Middle Indus river basin. In order to use TRMM satellite data for discharge measurement, its accuracy is determined by statistically comparing it with in situ gauged data on daily and monthly bases. The daily R 2 value (0.42) is significantly lower than monthly R 2 value (0.82), probably due to the time of summation of TRMM 3-hourly precipitation data into daily estimates. Daily TRMM data from 2003 to 2012 was used as input forcing in Soil and Water Assessment Tool (SWAT) hydrological model along with other input parameters. The calibration and validation results of SWAT model give R 2 = 0.72 and 0.73 and Nash-Sutcliffe coefficient of efficiency = 0.69 and 0.65, respectively. Daily and monthly comparison graphs are generated on the basis of model discharge output and observed data.

Satellite Rainfall Estimates for Debris Flow Prediction: An Evaluation Based on Rainfall Accumulation–Duration Thresholds

Abstract Rainfall thresholds are often used in early warning systems to identify rainfall conditions that, when reached or exceeded, are likely to result in debris flows. Rain gauges are typically used for the definition of these thresholds. However, in mountainous areas in situ observations are often sparse or nonexistent. Satellite-based rainfall estimates offer a solution to overcome the coverage problem at the global scale but are associated with significant estimation uncertainty. Evaluating satellite-based rainfall thresholds is thus necessary to understand their potential and limitations. In this work, an intercomparison among satellite-based precipitation products is presented in the context of estimating rainfall thresholds for debris flow prediction. The study is performed for the upper Adige River basin in the eastern Italian Alps during 2000–10. Large differences are observed between event-based characteristics (event duration and magnitude) derived from rain gauge and satellite-based estimates, revealing considerable interproduct variability in the debris flow–triggering rainfall characteristics. The parameters of the satellite-based thresholds differ less than 30% from the corresponding rain gauge–based parameters. Results further suggest that the adjustment of satellite-based estimates (either gauge based or by applying an error model) together with spatial resolution has an important impact on the estimation of the accumulation–duration thresholds.

Evaluation of Satellite-Based Rainfall Estimates and Application to Monitor Meteorological Drought for the Upper Blue Nile Basin, Ethiopia

Drought is a recurring phenomenon in Ethiopia that significantly impacts the socioeconomic sector and various components of the environment. The overarching goal of this study is to assess the spatial and temporal patterns of meteorological drought using a satellite-derived rainfall product for the Upper Blue Nile Basin (UBN). The satellite rainfall product used in this study was selected through evaluation of five high-resolution products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Climatology and Time-series (TARCAT) v2.0, Tropical Rainfall Measuring Mission (TRMM) and Africa Rainfall Estimate Climatology version 2 [ARC 2.0]). The statistical performance measuring techniques (i.e., Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and Bias) were used to evaluate the satellite rainfall products with the corresponding ground observation data at ten independent weather stations. The evaluation was carried out for 1998–2015 at dekadal, monthly, and seasonal time scales. The evaluation results of these satellite-derived rainfall products show there is a good agreement (r > 0.7) of CHIRPS and TARCAT rainfall products with ground observations in majority of the weather stations for all time steps. TARCAT showed a greater correlation coefficient (r > 0.70) in seven weather stations at a dekadal time scale whereas CHIRPS showed a greater correlation coefficient (r > 0.84) in nine weather stations at a monthly time scale. An excellent score of Bias (close to one) and mean error was observed in CHIRPS at dekadal, monthly and seasonal time scales in a majority of the stations. TARCAT performed well next to CHIRPS whereas PERSSIAN presented a weak performance under all the criteria. Thus, the CHIRPS rainfall product was selected and used to assess the spatial and temporal variability of meteorological drought in this study. The 3-month Z-Score values were calculated for each grid and used to assess the spatial and temporal patterns of drought. The result shows that the known historic drought years (2014–2015, 2009–2010, 1994–1995 and 1983–1984) were successfully indicated. Moreover, severe drought conditions were observed in the drought prone parts of the basin (i.e., central, eastern and southeastern). Hence, the CHIRPS rainfall product can be used as an alternative source of information in developing the grid-based drought monitoring tools for the basin that could help in developing early warning systems.

A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa

Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount—results that are comparable to the other datasets.

Assessing the temporal and spatial performance of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

Assessing the temporal and spatial performance of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

Application of GSMaP Product and Rain Gauge Data for Monitoring Rainfall Condition of Flood Events in Indonesia

Rainfall is one of main causes of floods. Monitoring of the rainfall is essential for understanding flood mechanism and early warning. Ground-based rain gauge is a conventional device to measure rainfall amount and considered as a point measurement. Satellite-based rainfall estimates provides complement measurement over wide area having few or even no in situ data. This study evaluates rainfall intensity variation and patterns preceding flood events in Indonesia for the period of 2003-2010 using the GSMaP_MVK satellite-based rainfall product with one hour and 0.1o x 0.1o resolutions and rain gauge station data as a benchmark. The analysed data are 3-hourly average and daily accumulation time steps. The chosen research locations were Medan City, Pekanbaru City, Indragiri Hulu Regency, Samarinda City and Manado City. Graphical comparisons of the GSMaP_MVK with the rain gauge data show discrepancies in capturing rainfall events and intensity. The GSMaP_MVK performs underestimation for the most areas, except Samarinda City, which is overestimated. Short-term period rainfall pattern is the most frequent occurred preceding flood events for the entire study areas which indicate that the areas are more susceptible to flash floods and river overflows. Overall, the GSMaP_MVK product provides promising potentiality for the application of monitoring rainfall conditions preceding flood events over the research locations. Statistical verifications reveal that on average, correlation coefficients are (0.22-0.54) and (0.65-0.83) for 3-hourly and daily scale, respectively. While, probability of rain detections (PODs) are (0.57-0.75) and (0.93-0.99), accordingly.

Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

Abstract. Accurate representation of the real spatio-temporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SREs) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.This study attempts to exhaustively evaluate – for the first time – the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1, and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25° grid if necessary). The modified Kling–Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.Results revealed that most SRE products performed better for the humid South (36.4–43.7° S) and Central Chile (32.18–36.4° S), in particular at low- and mid-elevation zones (0–1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (autumn and winter; MAM–JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no-rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere and for all timescales, which can be clearly attributed to its bias-correction procedure using 217 stations from Chile. Good results were also obtained by the research products CHIRPSv2, TMPA 3B42v7 and MSWEPv1.1, while CMORPH, PERSIANN-CDR, and the real-time PERSIANN-CCS-Adj were less skillful in representing observed rainfall. While PGFv3 (currently available up to 2010) might be used in Chile for historical analyses and calibration of hydrological models, the high spatial resolution, low latency and long data records of CHIRPS and TMPA 3B42v7 (in transition to IMERG) show promising potential to be used in meteorological studies and water resource assessments. We finally conclude that despite improvements of most SRE products, a site-specific assessment is still needed before any use in catchment-scale hydrological studies.

Evaluation of TRMM Multi-satellite Precipitation Analysis during the passage of Tropical Cyclones over Fiji

Fiji is prone to the devastating effects of heavy rainfall during the passage of tropical cyclones (TCs) and as such accurate measurement of rainfall during such events is urgent for effective disaster mitigation and risk analysis. Fiji, however, has a sparse distribution of rain gauges, thus there is a deficiency in the accurate measurement of rainfall. This gap could be filled by satellite-based rainfall estimates but before they are used, they need to be validated against a reference dataset for their accuracy and limitations. This study thus validates the TRMM based Multi-satellite Precipitation Analysis (TMPA) estimates over the island of Fiji. The study shows that TMPA has moderate skill in estimating rainfall during the passage of TCs over the island of Fiji. This skill is also highly variable as it decreases with an increase in rainfall intensity, increase in distance from the cyclone centre and increasing terrain elevation. The ability of TMPA also varies case by case but we report a general underestimation of rainfall by TMPA during the passage of TCs with a larger rainfall rate (defined in our case as those TCs with average daily rainfall greater than 25 mm day-1).

Modeling runoff with satellite-based rainfall estimates in the Niger basin

AbstractEffective runoff modeling in the Niger basin has been hampered by inadequate and deteriorating amount of reliable observation stations. Satellite-based rainfall products have increasingly been considered an important component in addressing these data gaps. We compared the Global Precipitation Climatology Project (GPCP) one degree daily estimate and interim reanalysis data of the European Centre for Medium-Range Weather Forecasts (ECMWF) named ERA-Interim rainfall estimates with observed rainfall. The suitability of the two rainfall products for runoff modeling was also assessed. Rainfall data were averaged over a well gauged catchment (Sota) and compared. They were subsequently used to calibrate a hydrological model and their modeling efficiencies were evaluated. The better of the two datasets was subsequently used in regional simulation on 10 Niger basin catchments. GPCP rainfall estimates had good fit to observed rainfall with Nash values of 0.93, 0.94 and 0.84 for monthly, seasonal and daily c...

Infilling Monthly Rain Gauge Data Gaps with Satellite Estimates for ASAL of Kenya

Design and operation of water resources management systems in sub-Saharan Africa suffer from inadequate observation data. Long running uninterrupted time series of data are often not available for water resource planning. Incomplete datasets with missing gaps is a challenge for users of the data. Inadequate data compromise results of analyses leading to wrong inference and conclusions of scientific assessments and research. Infilling of missing sections of data is necessary prior to the practical use of hydrometeorological time series. This paper proposes the use of Tropical Rainfall Measuring Mission satellite data as a viable alternate source of infill for missing rain gauge records. The least square regression method, using satellite-based estimates of rainfall was tested to fill in the missing data for 153 data points at nine rain gauge stations in Machakos, Makueni and the Kitui region of Kenya. Results suggest that the satellite rainfall estimates can be used as an alternative data source for rainfall series where the missing data gaps are large. The infilled data series were used in the development of monitoring, forecasting and drought early warning for Arid and Semi-Arid Lands (ASAL) in Kenya.

Assessing the role of uncertain precipitation estimates on the robustness of hydrological model parameters under highly variable climate conditions

Abstract Study region Four headwaters in Southern Africa. Study focus The streamflow regimes in Southern Africa are amongst the most variable in the world. The corresponding differences in streamflow bias and variability allowed us to analyze the behavior and robustness of the LISFLOOD hydrological model parameters. A differential split-sample test is used for calibration using seven satellite-based rainfall estimates, in order to assess the robustness of model parameters. Robust model parameters are of high importance when they have to be transferred both in time and space. For calibration, the modified Kling-Gupta statistic was used, which allowed us to differentiate the contribution of the correlation, bias and variability between the simulated and observed streamflow. New hydrological insights Results indicate large discrepancies in terms of the linear correlation ( r ), bias ( β ) and variability ( γ ) between the observed and simulated streamflows when using different precipitation estimates as model input. The best model performance was obtained with products which ingest gauge data for bias correction. However, catchment behavior was difficult to be captured using a single parameter set and to obtain a single robust parameter set for each catchment, which indicate that transposing model parameters should be carried out with caution. Model parameters depend on the precipitation characteristics of the calibration period and should therefore only be used in target periods with similar precipitation characteristics (wet/dry).

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region

Summary Following 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) version 6 are released. A critical evaluation of these newly released precipitation data sets is very important for both the end users and data developers. This study provides a comprehensive assessment of IMERG research product and GSMaP estimates over India at a daily scale for the southwest monsoon season (June to September 2014). The GPM-based precipitation products are inter-compared with widely used TRMM Multi-satellite Precipitation Analysis (TMPA), and gauge-based observations over India. Results show that the IMERG estimates represent the mean monsoon rainfall and its variability more realistically than the gauge-adjusted TMPA and GSMaP data. However, GSMaP has relatively smaller root-mean-square error than IMERG and TMPA, especially over the low mean rainfall regimes and along the west coast of India. An entropy-based approach is employed to evaluate the distributions of the selected precipitation products. The results indicate that the distribution of precipitation in IMERG and GSMaP has been improved markedly, especially for low precipitation rates. IMERG shows a clear improvement in missed and false precipitation bias over India. However, all the three satellite-based rainfall estimates show exceptionally smaller correlation coefficient, larger RMSE, larger negative total bias and hit bias over the northeast India where precipitation is dominated by orographic effects. Similarly, the three satellite-based estimates show larger false precipitation over the southeast peninsular India which is a rain-shadow region. The categorical verification confirms that these satellite-based rainfall estimates have difficulties in detection of rain over the southeast peninsula and northeast India. These preliminary results need to be confirmed in other monsoon seasons in future studies when the fully GPM-based IMERG retrospectively processed data prior to 2014 are available.

Evaluation of TRMM Multi-satellite Precipitation Analysis during the passage of Tropical Cyclones over Fiji

Fiji is prone to the devastating effects of heavy rainfall during the passage of tropical cyclones (TCs) and as such accurate measurement of rainfall during such events is urgent for effective disaster mitigation and risk analysis. Fiji, however, has a sparse distribution of rain gauges, thus there is a deficiency in the accurate measurement of rainfall. This gap could be filled by satellite-based rainfall estimates but before they are used, they need to be validated against a reference dataset for their accuracy and limitations. This study thus validates the TRMM based Multi-satellite Precipitation Analysis (TMPA) estimates over the island of Fiji. The study shows that TMPA has moderate skill in estimating rainfall during the passage of TCs over the island of Fiji. This skill is also highly variable as it decreases with an increase in rainfall intensity, increase in distance from the cyclone centre and increasing terrain elevation. The ability of TMPA also varies case by case but we report a general underestimation of rainfall by TMPA during the passage of TCs with a larger rainfall rate (defined in our case as those TCs with average daily rainfall greater than 25 mm day-1).

An evaluation and regional error modeling methodology for near-real-time satellite rainfall data over Australia

In providing uniform spatial coverage, satellite-based rainfall estimates can potentially benefit hydrological modeling, particularly for flood prediction. Maximizing the value of information from such data requires knowledge of its error. The most recent Tropical Rainfall Measuring Mission (TRMM) 3B42RT (TRMM-RT) satellite product version 7 (v7) was used for examining evaluation procedures against in situ gauge data across mainland Australia at a daily time step, over a 9 year period. This provides insights into estimating uncertainty and informing quantitative error model development, with methodologies relevant to the recently operational Global Precipitation Measurement mission that builds upon the TRMM legacy. Important error characteristics highlighted for daily aggregated TRMM-RT v7 include increasing (negative) bias and error variance with increasing daily gauge totals and more reliability at detecting larger gauge totals with a probability of detection of <0.5 for rainfall < ~3 mm/d. Additionally, pixel location within clusters of spatially contiguous TRMM-RT v7 rainfall pixels (representing individual rain cloud masses) has predictive ability for false alarms. Differences between TRMM-RT v7 and gauge data have increasing (positive) bias and error variance with increasing TRMM-RT estimates. Difference errors binned within 10 mm/d increments of TRMM-RT v7 estimates highlighted negatively skewed error distributions for all bins, suitably approximated by the generalized extreme value distribution. An error model based on this distribution enables bias correction and definition of quantitative uncertainty bounds, which are expected to be valuable for hydrological modeling and/or merging with other rainfall products. These error characteristics are also an important benchmark for assessing if/how future satellite rainfall products have improved.