Satellite-based Precipitation Estimates Research Articles

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.

Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA.

Component Analysis of Errors in Four GPM-Based Precipitation Estimations over Mainland China

As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, the latest GPM-era satellite-based precipitation estimations, including Global Satellite Mapping of Precipitation (GSMaP) and Integrated Multi-satellitE Retrievals for the GPM (IMERG), have been released. However, few studies have systematically evaluated these products over mainland China, although this is very important for both the end users and data developers. To these ends, the final-run uncalibrated IMERG V05 (V05UC), gauge-calibrated IMERG V05 (V05C) and IMERG V04 (V04C), and latest gauge-calibrated GSMaP V7 (GSMaP) are systematically evaluated and mutually compared against a merged product obtained from the China Meteorological Data Service Center via continuous statistical indices and an error decomposition analysis technology suite over mainland China from April 2014 to December 2016 at a 3 hourly scale and 0.1° × 0.1° resolution. The results show that, irrespective of the slight overestimation in the southeast and underestimation in the northern Tibetan Plateau, all four GSPEs could generally capture the spatial patterns of precipitation over mainland China. Meanwhile, the overall quality of the GSMaP is slightly superior to the IMERG products in east and south China; however, it also suffers from an overestimation of light rain and an underestimation of heavy rain. Such overestimation and underestimation are primarily from a large false precipitation in light rain and a negative hit bias in heavy rain, respectively. The latest IMERG V05 products have not shown significant improvement over the earlier version (V04C) in east and south China, but the calibrated V05C can best reproduce the probability density function in terms of precipitation intensity. Furthermore, V04C shows remarkable underestimation over the Tibetan Plateau, while this shortcoming has been resolved significantly in V05C. Alternately, the effects of the gauge calibration algorithm (GCA) used in IMERG are examined by comparison of V05UC and V05C. The results indicate that GCA cannot reduce the missed precipitation, and even enlarges the false precipitation over some regions. This reveals that GCA cannot effectively alleviate the bias resulting from the rain areas’ delineation and raining or not-raining detection. In addition, all of the products’ performance can be improved, particularly in the dry climate and high-latitude regions. This is a systematic estimation for GSPEs, providing deep insight into the characteristics and sources of error, and it could be valuable as a reference for both algorithm developers and data users, as well as for associated global products and various applications.

Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia

Precipitation is a key input variable for hydrological and climate studies. Rain gauges can provide reliable precipitation measurements at a point of observations. However, the uncertainty of rain measurements increases when a rain gauge network is sparse. Satellite-based precipitation estimations SPEs appear to be an alternative source of measurements for regions with limited rain gauges. However, the systematic bias from satellite precipitation estimation should be estimated and adjusted. In this study, a method of removing the bias from the precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping of gauge and satellite measurements over several climate zones as well as inverse-weighted distance for the interpolation of gauge measurements. Seven years (2010–2016) of daily precipitation estimation from PERSIANN-CCS was used to test and adjust the bias of estimation over Saudi Arabia. The first 6 years (2010–2015) are used for calibration, while 1 year (2016) is used for validation. The results show that the mean yearly bias is reduced by 90%, and the yearly root mean square error is reduced by 68% during the validation year. The experimental results confirm that the proposed method can effectively adjust the bias of satellite-based precipitation estimations.

TRMM-Based Optical and Microphysical Features of Precipitating Clouds in Summer Over the Yangtze–Huaihe River Valley, China

The optical and microphysical features of precipitating clouds are key information for studying the satellite-based precipitation estimation, cloud radiative effects, aerosol–cloud–precipitation interactions, cloud and precipitation parameterization in weather and climate models. In this study, 15-year synchronous spectral and radar observations from the TRMM satellite were used to statistically explore the optical and microphysical features of precipitating clouds (PCs), including cloud effective radius (CER), cloud optical thickness (COT), cloud water path (CWP), thermal infrared brightness temperature at channel 4 (TB4) of cloud top, and storm top height (STH) and their relationships with surface rain rates in summer over Yangtze–Huaihe River Valley (YHRV). Results show that the optical and microphysical features of PCs/stratiform PCs/convective PCs vary with geographical locations in summer over YHRV, due to the different ambient meteorological and topographical conditions. Higher CER/COT/CWP/STH and lower TB4 mainly locate at areas of bigger rain rates. For PCs, their spatial distribution of CER is mainly dominated by stratiform PCs, while their spatial distribution of COT/CWP is mainly dominated by convective PCs. Moreover, stratiform precipitation is the dominant form in summer over YHRV and, thus, most PCs present vertical structures of optical and microphysical features as stratiform PCs. Stratiform PCs are usually thicker and contain more water vapor with bigger cloud particles than convective PCs (including deep and shallow convective PCs). In addition, existing shallow convective PCs are associated with lower storm heights and warmer cloud tops. Finally, surface rain rates of PCs (convective/stratiform PCs) increase gradually with the increment of CER/COT/CWP/STH, especially under 5 (15/5) mm/h. Similar relationship between surface rain rates and COT/CWP for shallow convective PCs is also found under 0.75 mm/h. Surface rain rate of PCs (convective/stratiform PCs) with cold cloud tops (TB4 264 K), surface rain rate usually increases as CER decreases, which suggests that aerosol indirect effects are dominant in lower PCs, because over pollution regions abundant aerosols enter into lower clouds more easily and then suppress the development of shallow convective PCs.

Warm Season Satellite Precipitation Biases for Different Cloud Types Over Western North Pacific

Two along-track (level 2) satellite precipitation retrievals by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Dual Frequency Precipitation Radar Ku-band (DPR-Ku) and two multisatellite precipitation products, global satellite mapping of precipitation (GSMaP) and Integrated Multisatellite Retrievals for GPM (IMERG), are intercompared for different cloud types during warm season over the western North Pacific region. It is found that the biases of the precipitation measurements are systematically associated with cloud types. The best agreements of passive microwave (PMW) products and infrared-based (IR) products with satellite radar-based estimates are found for a relatively weak precipitation range for mid-low clouds (except over land) and high clouds, while similar agreement is found for heavier precipitation range for deep convection regardless of surface type. Precipitation from mid-low clouds over land is considerably underestimated by PMW and IR products over almost the entire intensity range. The IR-based precipitation estimates for deep convective clouds considerably overestimate the intensity for both weak precipitation and cases where precipitation was not detected by the DPR-Ku algorithm. The findings reveal the characteristics of the biases of the products depend on the associated cloud types, which suggests consideration of the cloud type information to improve satellite-based precipitation estimates.

The effect of rain gauge density and distribution on runoff simulation using a lumped hydrological modelling approach

Most lumped hydrological models use areal average precipitation data as model input. Though weather-radar-based and satellite-based precipitation estimation methods have been proposed in recent years, the rain gauge is still the most widely used precipitation-measuring tool. Optimal selection of rain gauge number and location will improve the accuracy of areal average precipitation estimations with minimum cost. In this study, the impacts of rain gauge density and distribution on lumped hydrological modelling uncertainty with different catchment sizes are analysed. To this end, the performances of a lumped hydrological model, the Xinanjiang model, in a densely gauged river basin, the Xiangjiang River basin, and its sub-basins under different gauge density and distribution are compared. First, seven levels of rain gauge density are defined. For each density level, several samples of different rain gauge distributions are randomly selected. Then, the areal average precipitation of each sample is estimated and used as input to the Xinanjiang model. Finally, the model is calibrated using the shuffled complex evolution (SCE-UA) algorithm, and model uncertainty is evaluated via the Bayesian method. The results show that 1) imperfect precipitation inputs measured by a sparse and irregular rain gauge network will lead to substantial uncertainty in model parameter estimation and flood simulation; 2) the impacts of imperfect precipitation estimates on model efficiency can be reduced to some extent through the adjustment of model parameters; 3) modelling uncertainty is reduced by increasing the rain gauge density or optimizing the rain gauge distribution pattern; and 4) the improvement in lumped model efficiency is no longer significant when the rain gauge density exceeds a certain threshold, but a further increase in rain gauge density will reduce model parameter uncertainty and the width of the runoff confidence interval.

Assessment of satellite-based precipitation estimates over Paraguay

Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations. Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA V7; 3B42 V7) and the version 1.0 of the purely satellite-based product of the Climate Prediction Center Morphing Technique (CMORPH RAW) through their comparison with daily in situ precipitation measurements from 1998 to 2012 over Paraguay. The statistical assessment is conducted with several commonly used indexes. Specifically, to evaluate the accuracy of daily precipitation amounts, mean error (ME), root mean square error (RMSE), BIAS, and coefficient of determination (R2) are used, and to analyze the capability to correctly detect different precipitation intensities, false alarm ratio (FAR), frequency bias index (FBI), and probability of detection (POD) are applied to various rainfall rates (0, 0.1, 0.5, 1, 2, 5, 10, 20, 40, 60, and 80 mm/day). Results indicate that TMPA V7 has a better performance than CMORPH RAW over Paraguay. TMPA V7 has higher accuracy in the estimation of daily rainfall volumes and greater precision in the detection of wet days (> 0 mm/day). However, both satellite products show a lower ability to appropriately detect high intensity precipitation events.

Error Analysis on PERSIANN Precipitation Estimations: Case Study of Urmia Lake Basin, Iran

AbstractIn-depth evaluation and analysis of the error properties associated with satellite-based precipitation estimation algorithms can play an important role in the future development and improve...

Spatial and temporal precipitation patterns characterized by TRMM TMPA over the Qinghai-Tibetan plateau and surroundings

ABSTRACTCompared with traditional methods to investigate spatial and temporal precipitation patterns over mountainous regions with limited meteorological observations, satellite-based precipitation estimates provide much more information to aid in understanding spatial distribution and variability, especially on the Qinghai-Tibetan Plateau and surrounding regions (TP), where the precipitation climate has been insufficiently documented so far. The monthly Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B43 V7 datasets, with high resolution (0.25°), were firstly applied in this study to investigate the spatial and temporal precipitation patterns over the TP, from 1998 to 2013. We found that the mean precipitation at the TP scale did not show clear trends at monthly, seasonal, or annual scales. We also found that precipitation over the TP shows different spatial variation trends at different temporal scales. For example, along the southern Himalayas, there is a clearly decreasing annual precipitation trend, with rates ranging from −120 to −20 mm/year, and the precipitation increases along the west coast of India to eastern Pamir, with rates larger than 20 mm/year. Another finding was that the precipitation climate over the TP is influenced by different atmospheric circulation patterns; the contribution of monthly and seasonal precipitation to total annual precipitation varies spatially. For instance, the annual precipitation along the southern Himalayas and the southeast TP mainly occurs in summer, and the northwest TP mainly receives precipitation in spring and winter.

Evaluation and hydrological application of satellite-based precipitation datasets in driving hydrological models over the Huifa river basin in Northeast China

Satellite-based precipitation estimates with high spatial and temporal resolution and large areal coverage have provided hydrologists a potential alternative source for hydrological applications since the last few years, especially for ungauged regions. This study evaluates five satellite-based precipitation datasets, namely, Fengyun, TRMM 3B42, TRMM 3B42RT, CMORPH_BLD and CMORPH_RAW, against gauge observations for streamflow simulation with a distributed hydrological model (SWAT) over the Huifa river basin, Northeast China. Results show that, by comparing the statistical indices (MA, M5P, STDE, ME, BIAS and CC) and inter-annual precipitation, it is demonstrated that Fengyun TRMM 3B42 and CMORPH_BLD show better agreement with gauge precipitation data than CMORPH_RAW and TRMM 3B42RT. When the SWAT model for each dataset calibrated and validated individually, satisfactory model performances (defined as: NS > 0.5) are achieved at daily scale for Fengyun, TRMM 3B42 and gauge-driven model, and very good performances (defined as: NS > 0.75) are achieved at monthly scale for Fengyun and gauge-driven model, respectively. The CMORPH_BLD forced daily simulations also yield higher values of NS and R2 than CMORPH_RAW and TRMM 3B42RT at daily and monthly step. From the uncertainty results, variations of P-factor values and frequency distribution curves of NS suggest that the simulation uncertainty increase when operating the Fengyun, 3B42RT, CMORPH_BLD and CMORPH_RAW-driven model with best fitted parameters for rain gauge SWAT model. The results also indicate that the influence of parameter uncertainty on model simulation results may be greater than the effect of input data accuracy. It is noted that uncertainty analysis is necessary to evaluate the hydrological applications of satellite-based precipitation datasets.

Satellite-based assessment of rainfall-triggered landslide hazard for situational awareness.

Determining the time, location, and severity of natural disaster impacts is fundamental to formulating mitigation strategies, appropriate and timely responses, and robust recovery plans. A Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to indicate potential landslide activity in near real-time. LHASA combines satellite-based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. Precipitation data from the Global Precipitation Measurement (GPM) mission are used to identify rainfall conditions from the past seven days. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a "nowcast" is issued to indicate the times and places where landslides are more probable. When LHASA nowcasts were evaluated with a Global Landslide Catalog, the probability of detection (POD) ranged from 8 to 60%, depending on the evaluation period, precipitation product used, and the size of the spatial and temporal window considered around each landslide point. Applications of the LHASA system are also discussed, including how LHASA is used to estimate long-term trends in potential landslide activity at a nearly global scale and how it can be used as a tool to support disaster risk assessment. LHASA is intended to provide situational awareness of landslide hazards in near real-time, providing a flexible, open source framework that can be adapted to other spatial and temporal scales based on data availability.

Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan

Recently, the Global Precipitation Measurement (GPM) mission has released the Integrated Multi-satellite Retrievals for GPM (IMERG) at a fine spatial (0.1° × 0.1°) and temporal (half hourly) resolutions. A comprehensive evaluation of this newly launched precipitation product is very important for satellite-based precipitation data users as well as for algorithm developers. The objective of this study was to provide a preliminary and timely performance evaluation of the IMERG product over the northern high lands of Pakistan. For comparison reference, the real-time and post real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products were also evaluated parallel to the IMERG. All of the selected precipitation products were evaluated at annual, monthly, seasonal and daily time scales using reference gauges data from April 2014 to December 2016. The results showed that: (1) the precipitation estimates from IMERG, 3B42V7 and 3B42RT products correlated well with the reference gauges observations at monthly time scale (CC = 0.93, 0.91, 0.88, respectively), whereas moderately at the daily time scale (CC = 0.67, 0.61, and 0.58, respectively); (2) Compared to the 3B42V7 and 3B42RT, the precipitation estimates from IMERG were more reliable in all seasons particularly in the winter season with lowest relative bias (2.61%) and highest CC (0.87); (3) IMERG showed a clear superiority over 3B42V7 and 3B42RT products in order to capture spatial distribution of precipitation over the northern Pakistan; (4) Relative to the 3B42V7 and 3B42RT, daily precipitation estimates from IMEREG showed lowest relative bias (9.20% vs. 21.40% and 26.10%, respectively) and RMSE (2.05 mm/day vs. 2.49 mm/day and 2.88 mm/day, respectively); and (5) Light precipitation events (0–1 mm/day) were usually overestimated by all said satellite-based precipitation products. In contrast moderate (1–20 mm/day) to heavy (>20 mm/day) precipitation events were underestimated by both TMPA products while IMERG was found capable to detect moderate to heavy precipitation events more precisely. Overall, the performance of IMERG was better than that of TMPA products. This preliminary evaluation of new generation of satellite-based precipitation estimates might be a useful feedback for sensor and algorithm developers as well as data users.

China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

Based on high-density gauge precipitation observations, high-resolution weather radar quantitative precipitation estimation (QPE) and seamless satellite-based precipitation estimates, a 1-km experimental gauge-radar-satellite merged precipitation dataset has been developed using the proposed local gauge correction (LGC) and optimal interpolation (OI) merging strategies. First, hourly precipitation analyses from approximately 40,000 automatic weather stations at 0.01° resolution were used to correct bias in the radar QPE Group System (QPEGS), developed by the China Meteorological Administration (CMA) and the Climate Prediction Center Morphing (CMORPH) precipitation products. As precipitation events tend to have a more localized distribution at the hourly and 0.01° resolutions, three core parameters were improved using the OI method. (a) The spatial dependence of the error variance for radar QPE was accounted for over six sub-regions in China and is shown as a non-linear function of the gauge precipitation analysis. (b) The spatial dependence of error correlation for the radar QPE decreased exponentially with distance. (c) The error of the hourly gauge-based precipitation analysis was quantified as a function of the precipitation amount and the gauge network density, using the Monte Carlo method to randomly sample the gauge observations over the dense gauge network. The performance of the 1-km experimental gauge-radar-satellite merged precipitation dataset (named as China Merged Precipitation Analysis: CMPA_1km) was assessed at 6 h-temporal resolutions and 0.03° × 0.03° spatial resolution using precipitation observations from 208 independent hydrological stations as a reference. Compared with radar QPE and CMORPH, the CMPA-1km showed obviously better accuracy in all sub-regions and during all seasons. In contrast, gauge analysis and CMPA-1km shared similar accuracy, but the latter could estimate heavy precipitation more accurately than the former, as well as the latter has the advantage of seamless spatial coverage. However, the CMPA-1km exhibits larger uncertainty during the cold season compared to the warm season, which will need further improvement in future work. The downscaled bias-corrected 0.01° resolution CMORPH was employed to fill the gaps in regions, mainly in Western China and the Tibetan Plateau, where gauge and radar measurements are limited.

Improving TMPA 3B43 V7 Data Sets Using Land-Surface Characteristics and Ground Observations on the Qinghai–Tibet Plateau

The accurate knowledge of precipitation information over the Qinghai–Tibet Plateau, where the rain gauge networks are limited, is vital for various applications. While satellite-based precipitation estimates provide high spatial resolution (0.25°), large uncertainties and systematic anomalies still exist over this critical area. To derive more accurate monthly precipitation estimates, a spatial data-mining algorithm was used to remove the obvious anomalies compared with their neighbors from the original Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) 3B43 V7 data at an annual scale, as the TMPA data are more accurate than other satellite-based precipitation estimates. To supplement the international exchange stations, additional ground observations were used to calibrate and improve the TMPA data with anomalies removed at an annual scale. Finally, a disaggregation strategy was adopted to derive monthly precipitation estimates based on the calibrated TMPA data. We concluded that: 1) the obvious anomalies compared with their neighbors could be removed from the original TMPA data sets and 2) the calibrated results were of a higher quality than the original TMPA data in each month from 2000 to 2013. The improved TMPA 3B43 V7 data sets over the Qinghai–Tibet plateau, named NITMPA3B43_QTP, are available at http://agri.zju.edu.cn/NITMPA3B43_QTP/ .

A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information

AbstractCompared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estimation from bispectral information is designed, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the nonzero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to delineate precipitation regions precisely. In the second stage, the model aims to estimate the pointwise precipitation amount accurately while preserving its heavily skewed distribution. Stacked denoising autoencoders (SDAEs), a commonly used deep learning method, are applied in both stages. Performance is evaluated along a number of common performance measures, including both R/NR and real-valued precipitation accuracy, and compared with an operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). For R/NR binary classification, the proposed two-stage model outperforms PERSIANN-CCS by 32.56% in the critical success index (CSI). For real-valued precipitation estimation, the two-stage model is 23.40% lower in average bias, is 44.52% lower in average mean squared error, and has a 27.21% higher correlation coefficient. Hence, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation product. The authors also provide some future directions for development of satellite-based precipitation estimation products in both incorporating auxiliary information and improving retrieval algorithms.

Flood modelling using satellite-based precipitation estimates and digital elevation model in eastern Iraq

Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. The study conducted in Wasit province/Eastern Iraq when a flood occurs due to heavy rainfall in May 2013. In this study the capability of Tropical Rainfall Measuring Mission (TRMM) rainfall daily data have been used to estimate the relationship between measured precipitation and the Digital Elevation Model (DEM), also to study the relationship between rainfall intensity and flood waters areas. Rainfall estimation by remote sensing using satellite-derived data from the Tropical Rainfall Measuring Mission (TRMM) is a possible means of supplementing rain gauge data, having the better spatial cover of rainfall fields. The approach used throughout this paper has integrated recently compiled data derived from satellite imagery (rainfall, and digital elevation model) into a GIS geodatabase to study the relationship between rainfall intensity and floodwater's areas then the results' comparison with the Normalized Difference Water Index (NDWI) after the flood. ArcGIS software has been used to process, analyze the archived Tropical Rainfall Measuring Mission (TRMM) precipitation data, and calculate NDWI from Landsat 8 images. In conclusions, the study explains the flood-area clearly captured by the TRMM measurements; and the region’s water increased. Also, good correlation between measured precipitation and the Digital Elevation Model (DEM) has been detected.

Evaluation of Satellite-Based Precipitation Products from IMERG V04A and V03D, CMORPH and TMPA with Gauged Rainfall in Three Climatologic Zones in China

A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product’s continued development and future improvement. To these ends, the four precipitation estimates including IMERG (the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement) V04A, IMERG V03D, CMORPH (the Climate Prediction Center Morphing technique)-CRT and TRMM (the Tropical Rainfall Measuring Mission) 3B42 are systematically evaluated against the gauge precipitation estimates at multiple spatiotemporal scales from 1 June 2014 to 30 November 2015 over three different topographic and climatic watersheds in China. Meanwhile, the statistical methods are utilized to quantize the performance of the four satellite-based precipitation estimates. The results show that: (1) over the Tibetan Plateau cold region, among all products, IMERG V04A underestimates precipitation with the largest RB (−46.98%) during the study period and the similar results are seen at the seasonal scale. However, IMERG V03D demonstrates the best performance according to RB (7.46%), RMSE (0.44 mm/day) and RRMSE (28.37%). Except for in summer, TRMM 3B42 perform better than CMORPH according to RMSEs, RRMSEs and Rs; (2) within the semi-humid Huaihe River Basin, IMERG V04A has a slight advantage over the other three satellite-based precipitation products with the lowest RMSE (0.32 mm/day) during the evaluation period and followed by IMERG V03D, TRMM 3B42 and CMORPH orderly; (3) over the arid/semi-arid Weihe River Basin, in comparison with the other three products, TRMM 3B42 demonstrates the best performance with the lowest RMSE (0.1 mm/day), RRMSE (8.44%) and highest R (0.92) during the study period. Meanwhile, IMERG V03D perform better than IMERG V04A according all the statistical indicators; (4) in winter, IMERG V04A and IMERG V03D tend to underestimate the total precipitation with RBs (−70.62% vs. −6.47% over the Tibetan Plateau, −46.92% vs. −0.66% over the Weihe River Basin, respectively); and (5) overall, except for IMERG V04A in Tibetan Plateau, all satellite-based precipitation captured the gauge-based precipitation well over the three regions according to RRMSEs, Rs and Rbs during the study period. IMERG V03D performs better than its predecessors-TRMM 3B42 and CMORPH over the Tibetan Plateau region and the Huaihe River Basin, while IMERG V04A only does so over the latter. Between the two IMERG products, IMERG V04A does not show an advantage over IMERG V03D over the Tibetan Plateau region and the Weihe River Basin. In particular, over the former, IMERG V04A performs far worse than IMERG V03D. These findings provide valuable feedback for both IMERG algorithm developers and data users.

How accurate are satellite estimates of precipitation over the north Indian Ocean?

Following the launch of the Global Precipitation Measurement (GPM) Core Observatory in early 2014, motivated from the successful Tropical Rainfall Measurement Mission (TRMM) satellite, an advanced and sophisticated global multi-satellite precipitation product–Integrated Multi-satellitE Retrievals for GPM (IMERG) was released at finer spatio-temporal scales. This precipitation product has been upgraded recently after several refinements and supposed to be superior to other existing global or quasi-global multi-satellite precipitation estimates. In the present study, IMERG precipitation is comprehensively evaluated for the first time against moored buoy observations over the north Indian Ocean at hourly scale for the study period of March 2014 to December 2015. IMERG precipitation performs considerably better over the Bay of Bengal than the Arabian Sea in both detection and estimation. The systematic error in IMERG is appreciably lower by about 14%; however, it generally overestimates in-situ precipitation and also exhibits noticeable false alarms. Furthermore, IMERG essentially shows an improvement over the TRMM Multi-satellite Precipitation Analysis (TMPA) at a daily scale over the north Indian Ocean. IMERG precipitation estimates show overall promising error characteristics, but there is still a need of substantial efforts for improvement in the satellite-based precipitation estimation algorithms especially over data sparse regions such as north Indian Ocean.

Simulation of Ship-Track versus Satellite-Sensor Differences in Oceanic Precipitation Using an Island-Based Radar

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h − 1 (55%) compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24–34 km h − 1 depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data.