TRMM) Multi-satellite Precipitation Analysis Products Research Articles

From TRMM to GPM, how do improvements of post/near-real-time satellite precipitation estimates manifest?

With the end of the Tropical Rainfall Measuring Mission (TRMM) era, its successor of Global Precipitation Measurement (GPM) aims to provide a new generation of satellite precipitation estimates (SPE) with higher spatiotemporal resolution and wider spatial coverage. As the representative precipitation retrieval algorithms for GPM and TRMM eras, it is necessary to compare the Integrated Multi-satellite Retrievals for GPM (IMERG) with the TRMM Multi-satellite Precipitation Analysis (TMPA) to investigate the improvements of SPEs from TRMM to GPM. This study analyzes the post/near-real-time SPEs from TMPA and IMERG over Mainland China from April 2014 to March 2018 at daily scale, using precipitation observations from 510 meteorological stations as reference. A very comprehensive evaluation system, which contains four indicators of continuous metrics, categorical metrics, error components, and systematic and random errors is adopted in this study. Besides, four patterns with spatial distribution, temporal pattern, precipitation intensity distribution, and overall metrics are applied to illustrate each indicator's performance. The results show that IMERG products have lower RB and RMSE and higher CC than TMPA and greatly reduce the three error components (hit bias, missed precipitation, and false precipitation). Furthermore, IMERG products present a higher capability to capture the precipitation events correctly and generally have lower systematic and random errors than TMPA products. However, there are still rooms for IMERG products to improve, such as higher false alarm ratio (FAR) and more false precipitation of IMERG products at relatively low rain rates, the 25% higher systematic error of IMERG-E (IMERG “Early Run” near-real-time SPE) compared to TMPA-RT (TMPA near-real-time SPE). In addition, we find the gauge adjustment from GPCC is not a very effective way to improve the detecting capability and reduce the systematic errors for near-real-time SPEs over Mainland China. This study provides some valuable information on the transition from TMPA to IMERG, which may advance the algorithm's development and the hydrometeorological applications.

Performance evaluation and correction of precipitation data using the 20-year IMERG and TMPA precipitation products in diverse subregions of China

The latest Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) dataset version 6, which is the successor of the multi-satellite 3B42 dataset from the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), is now recommended for many purposes. It is unclear how the quality of the latest IMERG data products compares to that of the delisted TMPA series data sets. This study evaluated the accuracy of TRMM 3B42 version 7 and IMERG final version 6 products at 677 stations in the 7 subregions of China over 2000–2019 at the daily, monthly and annual timescales based on gauge-observed precipitation data. The performance of TMPA and IMERG precipitation products was assessed by six statistical indices. The product with the best performance was corrected based on the linear relationship of monthly precipitation between remote sensing products and ground-observed precipitation. The correction was then used for the predicting and mapping of precipitation in July 2019. The results showed that (1) IMERG outperformed TMPA at all three timescales, and the accuracy increased with the expanding timescale. (2) IMERG exhibited better performance in all subregions than TMPA and performed best in the mid-temperate semihumid zone and worst in the mid-temperature arid zone. (3) IMERG showed higher accuracy than TMPA in all months for most subregions and mainland China but performed worse in dry (December, January and February) and wet (July, August and September) months. (4) The linear relationship between remote sensing products and ground-observed precipitation could be used to correct and predict satellite precipitation data without up-to-date gauge observations, the predicted map indicated the good spatial variation in precipitation and better accuracy than the original IMERG data. In conclusion, the IMERG dataset was more accurate and could be used for precipitation investigations at large scales in China.

Applicability and Hydrologic Substitutability of TMPA Satellite Precipitation Product in the Feilaixia Catchment, China

The assessment of various precipitation products’ performances in extreme climatic conditions has become a topic of interest. However, little attention has been paid to the hydrological substitutability of these products. The objective of this study is to explore the performance of the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) product in the Feilaixia catchment, China. To assess its applicability in extreme consecutive climates, several statistical indices are adopted to evaluate the TMPA performance both qualitatively and quantitatively. The Cox–Stuart test is used to investigate extreme climate trends. The Soil and Water Assessment Tool (SWAT) model is used to test the TMPA hydrological substitutability via three scenarios of runoff simulation. The results demonstrate that the overall TMPA performance is acceptable, except at high-latitudes and locations where the terrain changes greatly. Moreover, the accuracy of the SWAT model is high both in the semi-substitution and full-substitution scenarios. Based on the results, the TMPA product is a useful substitute for the gauged precipitation in obtaining acceptable hydrologic process information in areas where gauged sites are sparse or non-existent. The TMPA product is satisfactory in predicting the runoff process. Overall, it must be used with caution, especially at high latitudes and altitudes.

Monitoring the Indian Summer Monsoon Evolution at the Granularity of the Indian Meteorological Sub-divisions using Remotely Sensed Rainfall Products

We make use of satellite-based rainfall products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to objectively define local onset and demise of the Indian Summer Monsoon (ISM) at the spatial resolution of the meteorological subdivisions defined by the Indian Meteorological Department (IMD). These meteorological sub-divisions are the operational spatial scales for official forecasts issued by the IMD. Therefore, there is a direct practical utility to target these spatial scales for monitoring the evolution of the ISM. We find that the diagnosis of the climatological onset and demise dates and its variations from the TMPA product is quite similar to the rain gauge based analysis of the IMD, despite the differences in the duration of the two datasets. This study shows that the onset date variations of the ISM have a significant impact on the variations of the seasonal length and seasonal rainfall anomalies in many of the meteorological sub-divisions: for example, the early or later onset of the ISM is associated with longer and wetter or shorter and drier ISM seasons, respectively. It is shown that TMPA dataset (and therefore its follow up Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)) could be usefully adopted for monitoring the onset of the ISM and therefore extend its use to anticipate the potential anomalies of the seasonal length and seasonal rainfall anomalies of the ISM in many of the Indian meteorological sub-divisions.

Validation of Integrated MultisatellitE Retrievals for GPM (IMERG) by using gauge-based analysis products of daily precipitation over East Asia

This paper documents the qualities of satellite-based daily precipitation products from Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over East Asia. Evaluations for a year from June 2014 to May 2015 are performed using gauge-based precipitation analysis from the Climate Prediction Center (CPC) and Global Precipitation Climatology Centre (GPCC) and other satellite-based high-resolution precipitation products, such as Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) and the Climate Prediction Center Morphing Method (CMORPH), are also compared. The results indicate that satellite products effectively capture seasonal variations in precipitation over East Asian land regions from spring to fall although overall underestimation with relatively low correlations is observed during winter. The verification of daily detection for various thresholds indicates that IMERG and TMPA products exhibit similarly higher correspondence to CPC while CMORPH exhibits persistent underestimation for all thresholds and especially in winter. The IMERG and TMPA products tend to underestimate with decreasing thresholds and overestimate with increasing thresholds against CPC, although this tendency is significantly reduced when validated with GPCC. Nevertheless, the overall performance of IMERG and TMPA are comparable and IMERG shows reliable performance in daily precipitation for all seasons, indicating less bias and higher skill scores against those of gauge-based precipitations. The assessment study suggests the validity of the IMERG product for daily precipitation over East Asia, and this exhibits the potential for use as reference precipitation data to validate numerical weather prediction models.

Evaluation of Three-Hourly TMPA Rainfall Products Using Telemetric Rain Gauge Observations at Lai Nullah Basin in Islamabad, Pakistan

Flash floods which occur due to heavy rainfall in hilly and semi-hilly areas may prove deleterious when they hit urban centers. The prediction of such localized and heterogeneous phenomena is a challenge due to a scarcity of in-situ rainfall. A possible solution is the utilization of satellite-based precipitation products. The current study evaluates the efficacy of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) three-hourly products, i.e., 3B42 near-real-time (3B42RT) and 3B42 research version (3B42V7) at a sub-daily time scale. Various categorical indices have been used to assess the capability of products in the detection of rain/no-rain. Hourly rain rates are assessed by employing the most commonly used statistical measures, such as correlation coefficients (CC), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). Further, a diurnal analysis is performed to authenticate TMPA’s performance in specific hours of the day. In general, the results show the good capability of both TMPA products in the detection of rain/no-rain events in all seasons except winter. Specifically, 3B42V7 performed better than 3B42RT. Moreover, both products detect a high number of rainy days falsely in light rain ranges. Regarding rainfall measurements, TMPA products exhibit an overall underestimation. Seasonally, 3B42V7 underestimates rainfall in monsoon and post-monsoon, and overestimates in winter and pre-monsoon. 3B42RT, on the other hand, underestimates rainfall in all seasons. A greater MBE and RMSE are found with both TMPA rain measurements in monsoon and post-monsoon seasons. Overall, a weak correlation and high MBE between the TMPA (3B42RT, 3B42V7) and reference gauge hourly rain rates are found at a three-hourly time scale (CC = 0.41, 0.38, MBE = −0.92, −0.70). The correlation is significant at decadal (CC = 0.79, 0.77) and monthly (CC = 0.91, 0,90) timescales. Furthermore, diurnal rainfall analysis indicates low credibility of 3B42RT to detect flash flooding. Within the parameters of this study, we conclude that the TMPA products are not the best choice at a three-hourly time scale in hilly/semi-hilly areas of Pakistan. However, both products can be used at daily, yet more reliably above daily, time scales, with 3B42V7 preferable due to its consistency.

Comparisons of Gauge, TMPA and IMERG Products for Monsoon and Tropical Cyclone Precipitation in Southern China

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) Mission (IMERG) products are compared against 447 quality-controlled rain gauges in southern China (SC) during summer (May to August) for the period 2014–2016. The differences of TMPA and IMERG in measuring the monsoon and tropical cyclone (TC) precipitation, considering the mean spatial patterns, and rainfall intensities are evaluated quantitatively. Statistical analysis shows that IMERG has much higher correlations of precipitation frequency (PF) with gauge observations for monsoon and TC precipitation. However, both satellite-based products show much poorer capability in estimating conditional rainfall and PF for TC precipitation, especially for estimating large conditional rainfall in TC systems. The capability of capturing the precipitation events at different rainfall intensities decreases with the increase of rainfall threshold for both monsoon and TC precipitation. Generally, IMERG exhibits a superior ability to detect precipitation at different rainfall intensities than TMPA. In depicting the diurnal cycles, IMERG shows a much better performance in estimating rainfall and PF for monsoon precipitation than TMPA by revealing the comparable diurnal amplitudes to the rain gauges, whereas TMPA fails to accurately estimate the early morning rainfall and PF. As a successor to TMPA, IMERG is a reliable source of precipitation estimates for future studies on precipitation meteorology.

On the rainfall asymmetry and distribution in tropical cyclones over Bay of Bengal using TMPA and GPM rainfall products

In the present study, we have used TRMM multi-satellite precipitation analysis (TMPA) 3B42 and global precipitation mission (GPM): IMERG “precipitation Cal” products to identify and quantify the asymmetry and distribution of rainfall in tropical cyclones formed over Bay of Bengal (BoB). For the period 2010–2013, TMPA products have been made use of and for the period 2014–2017, GPM–IMERG products are used to study the aforementioned features. Overall, 17 cyclones covering 75 events/days ranging from depression to very severe cyclonic stage of the system have been analysed. Our analysis revealed some interesting features on asymmetry, direction of maximum rain-receiving zones, relation of T-number with maximum and total rainfall in rain-receiving areas. The study reveals the direction of rainfall zone is mostly towards west and southwest directions of the storm centre in BoB. This point gains importance in the context of earlier reports, where it is mentioned that the direction of maximum rainfall is in east. The study also infers that the maximum rainfall and total rainfall need not be dependent on the intensity (indicated by T-number). We also attempted to classify the rain-receiving zones, based on both the rainfall-rate window and area covered in each window of rainfall, and a criterion has been proposed. The distribution of rainfall is classified, as sharply falling, slowly falling and nearly constant.

Performance of the Standardized Precipitation Index Based on the TMPA and CMORPH Precipitation Products for Drought Monitoring in China

This paper evaluated the accuracy of multiple satellite-based precipitation products including the tropical rainfall measuring mission multisatellite precipitation analysis (TMPA) (TMPA 3B42RT and TMPA 3B42 version 7) and the Climate Prediction Center MORPHing technique (CMORPH) (CMORPH RAW and CMORPH BLD version 1.0) datasets and investigated the impact of the accuracy and temporal coverage of these data products on the reliability of the standardized precipitation index (SPI) estimates. The satellite-based SPI was compared with the SPI estimate using in situ precipitation observations from 2221 meteorological observation sites across China from 1998 to 2014. The SPI values calculated from the products calibrated with rain gauge measurements (TMPA 3B42 and CMORPH BLD) are generally more consistent with the SPI obtained with in situ measurements than those obtained using noncalibrated products (TMPA 3B42RT and CMORPH RAW products). The short data record of satellite precipitation data products is not the primary source of large errors in the SPI estimates, suggesting that the SPI estimate using satellite precipitation data products can be applied to drought assessment and monitoring. Satellite-based SPI estimates are more accurate in eastern China than in western China because of larger uncertainties in precipitation retrievals in western China, characterized by arid and semiarid climate conditions and complex landscapes. The satellite-based SPI can capture typical drought events throughout China, with the limitation that it is based on precipitation only and that different durations of antecedent precipitation are only suitable for specific drought conditions.

Assessment of the GPM and TRMM Precipitation Products Using the Rain Gauge Network over the Tibetan Plateau

Using high-quality hourly observations from national-level ground-based stations, the satellite-based rainfall products from both the Global Precipitation Measurement (GPM) Integrated MultisatellitE Retrievals for GPM (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), are statistically evaluated over the Tibetan Plateau (TP), with an emphasis on the diurnal variation. The results indicate that: (1) the half-hourly IMERG rainfall product can explicitly describe the diurnal variation over the TP, but with discrepancies in the timing of the greatest precipitation intensity and an overestimation of the maximum rainfall intensity over the whole TP. In addition, the performance of IMERG on the hourly timescale, in terms of the correlation coefficient and relative bias, is different for regions with sea level height below or above 3500 m; (2) the IMERG products, having higher correlation and lower root-mean-square error, perform better than the TMPA products on the daily and monthly timescales; and (3) the detection ability of IMERG is superior to that of TMPA, as corroborated by a higher Hanssen and Kuipers score, a higher probability of detection, a lower false alarm ratio, and a lower bias. Compared to TMPA, the IMERG products ameliorate the overestimation across the TP. In conclusion, GPM IMERG is superior to TRMM TMPA over the TP on multiple timescales.

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.

Hydrologic Evaluation of TRMM Multisatellite Precipitation Analysis for Nanliu River Basin in Humid Southwestern China

The accuracy of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) daily accumulated precipitation products (3B42RTV7 and 3B42V7) was evaluated for a small basin (the Nanliu river basin). A direct comparison was performed against gauge observations from a period of 9 years (2000–2009) at temporal and spatial scales. The results show that the temporal-spatial precipitation characteristics of the Nanliu river basin are highly consistent with 3B42V7 relative to 3B42RTV7, with higher correlation coefficient (CC) approximately 0.9 at all temporal scales except for the daily scale and a lower relative bias percentage. 3B42V7 slightly overestimates precipitation at all temporal scales except the yearly scale; it slightly underestimates the precipitation at the daily spatial scale. The results also reveal that the precision of TMPA products increases with longer time-aggregated data, and the detection capability of daily TMPA precipitation products are enhanced by augmentation with daily precipitation rates. In addition, daily TMPA products were input into the Xin’anjiang hydrologic model; the results show that 3B42V7-based simulated outputs were well in line with actual stream flow observations, with a high CC (0.90) and Nash-Sutcliffe efficiency coefficient (NSE, 0.79), and the results adequately captured the pattern of the observed flow curve.

Hydrological Utility and Uncertainty of Multi-Satellite Precipitation Products in the Mountainous Region of South Korea

Satellite-derived precipitation can be a potential source of forcing data for assessing water availability and managing water supply in mountainous regions of East Asia. This study investigates the hydrological utility of satellite-derived precipitation and uncertainties attributed to error propagation of satellite products in hydrological modeling. To this end, four satellite precipitation products (tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TMPA) version 6 (TMPAv6) and version 7 (TMPAv7), the global satellite mapping of precipitation (GSMaP), and the climate prediction center (CPC) morphing technique (CMORPH)) were integrated into a physically-based hydrologic model for the mountainous region of South Korea. The satellite precipitation products displayed different levels of accuracy when compared to the intra- and inter-annual variations of ground-gauged precipitation. As compared to the GSMaP and CMORPH products, superior performances were seen when the TMPA products were used within streamflow simulations. Significant dry (negative) biases in the GSMaP and CMORPH products led to large underestimates of streamflow during wet-summer seasons. Although the TMPA products displayed a good level of performance for hydrologic modeling, there were some over/underestimates of precipitation by satellites during the winter season that were induced by snow accumulation and snowmelt processes. These differences resulted in streamflow simulation uncertainties during the winter and spring seasons. This study highlights the crucial need to understand hydrological uncertainties from satellite-derived precipitation for improved water resource management and planning in mountainous basins. Furthermore, it is suggested that a reliable snowfall detection algorithm is necessary for the new global precipitation measurement (GPM) mission.

Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China

The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25° × 0.25° resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA’s systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter.

Evaluation of latest TMPA and CMORPH precipitation products with independent rain gauge observation networks over high-latitude and low-latitude basins in China

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH) are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products (i.e., Version-7 real-time TMPA (T-rt) and gauge-adjusted TMPA (T-adj), and Version-1.0 real-time CMORPH (C-rt) and Version-1.0 gauge-adjusted CMORPH (C-adj)) are evaluated and intercompared by using independent rain gauge observations for a 12-year (2000–2011) period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite precipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.

Statistical and Hydrological Comparisons between TRMM and GPM Level-3 Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA 3B42V7?

Abstract The goal of this study is to quantitatively intercompare the standard products of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and its successor, the Global Precipitation Measurement (GPM) mission Integrated Multisatellite Retrievals for GPM (IMERG), with a dense gauge network over the midlatitude Ganjiang River basin in southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of the rainy season (from May through September 2014) at 0.25° and daily resolutions show that all three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both of the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42V7 are comparable at grid and basin scales. Hydrologic assessment with the Coupled Routing and Excess Storage (CREST) hydrologic model indicates that the Day-1 IMERG product performs comparably to gauge reference data. In many cases, the IMERG product outperforms TMPA standard products, suggesting a promising prospect of hydrologic utility and a desirable hydrologic continuity from TRMM-era product heritages to GPM-era IMERG products. Overall, this early study highlights that the Day-1 IMERG product can adequately substitute TMPA products both statistically and hydrologically, even with its limited data availability to date, in this well-gauged midlatitude basin. As more IMERG data are released, more studies to explore the potential of GPM-era IMERG in water, weather, and climate research are urgently needed.

Error characterization of TRMM Multisatellite Precipitation Analysis (TMPA-3B42) products over India for different seasons

Summary A comprehensive error characterization of satellite-based precipitation products is vital for advancement of precipitation algorithms, evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products are in high demand to users for numerous applications. The latest version 7 (V7) of TMPA products are assumed to be better than its previous versions due to substantial changes in input data sets and algorithm. The TMPA algorithm is also a benchmark algorithm for the Global Precipitation Measurement (GPM) mission multisatellite precipitation products. In this study, both near-real-time (TMPA-RT) and research-quality (TMPA-V7) TMPA data sets are extensively evaluated against gridded gauge-based data over India for pre-monsoon, monsoon, and post-monsoon seasons at daily scale for a 13-year (2001–2013) period. The systematic and random error components in TMPA data sets are decomposed for their wider usage in agricultural and hydrological applications. Although TMPA-V7 and RT data sets represent the mean seasonal rainfall characteristics reasonably well, both the satellite-based data sets show an overestimation of rainfall over most parts of the country except over the orographic regions. However, TMPA-V7 is better than TMPA-RT when compared with the reference observations. Both the TMPA products show larger systematic error over the mountainous regions of northeast India. TMPA-RT has larger random error than TMPA-V7 possibly due to difference in calibration methods for the development of these data sets. Both the satellite-based data sets show significant difference from the reference observations for light to moderate rainfall ranges especially in the monsoon season. Larger error associated with systematic component in both the data sets is found during the pre-monsoon season. Furthermore, bias and error in satellite-derived rainfall data show a considerable interannual variation. Overall results suggest that a suitable region and season dependent bias-correction is required in TMPA data sets before its integration in hydrological applications.

Hydrologic Evaluation of the TRMM Multisatellite Precipitation Analysis Over Ganjiang Basin in Humid Southeastern China

This study assesses the successive Version-6 and Version-7 TRMM Multisatellite Precipitation Analysis (TMPA) products including near-real-time products (3B42RTV6 and 3B42RTV7) and post-real-time research products (3B42V6 and 3B42V7) from March 2002 to February 2008 over Ganjiang Basin in the humid southeastern China, located in the lower reach of Yangtze River. Direct comparison of TMPA rainfall estimates with ground observation shows that the spatial and temporal rainfall characteristics over this region are well captured by 3B42V6, 3B42RTV7, and 3B42V7. In terms of daily grid-based comparison, 3B42RTV7 has been improved over 3B42RTV6 by reducing relative bias (RB) from -30.25% to 4.93%; 3B42V6, 3B42RTV7, and 3B42V7 show close performance with each other with RB less than 5% and moderate correlative coefficient (CC, 0.59). Daily hydrologic simulation with Xin'anjiang hydrologic model using these TMPA products as input shows that: 1) 3B42V6 and 3B42V7 demonstrate very comparable hydrologic skills, which are close to those of the reference rainfall with high Nash-Sutcliffe index (NSCE, 0.71 and 0.72, respectively) and strong correlation (CC = 0.88); and 2) 3B42RTV7 displays a better hydrologic performance than 3B42RTV6 by increasing the NSCE from 0.56 to 0.59, improving CC from 0.81 to 0.87, and reducing RB from -33.15% to 20.93%. This improvement of real-time TMPA product shows its potential hydrologic utility in water resource management and flood forecast. Finally, this study provides useful reference for TMPA developers and insights for the end users in their applications.

Spatial and Diurnal Variability of Monsoon Systems Assessed by TRMM Rain Rate Over Indus Basin

The spatiotemporal characteristics of the Asian monsoon over the Indus River basin are studied using the latest near real-time satellite precipitation estimates from the tropical rainfall measuring mission (TRMM) multisatellite precipitation analysis (TMPA). The TMPA data product (3B42RT V7) is used to analyze diurnal variability of the Asian monsoon over the study domain during January 2005 to December 2010. First, the spatiotemporal uncertainty of satellite estimates is systematically characterized by comparison to rain gauge observations using four standard error metrics, i.e., the Pearson correlation coefficient (CC), root-mean-square error (RMSE), mean absolute error (MAE), and relative bias (BIAS). Second, diurnal rainfall variability over selected regions is investigated by comparing rainfall patterns during premonsoon and monsoon seasons. The comparison and evaluation of satellite-based estimates and rain gauge data revealed significant correlation of 0.87 for the stations in the southwest and 0.63 in the northeast monsoon region. The results indicated TMPA underestimates over the intense monsoon region from $- \bf{8}\% $ to $- \bf{25}\% $ , while there is an overestimation over the southern region from 7% to 35%. This study improves understanding on the rainfall diurnal variations captured by the three hourly TMPA products during the April–June (premonsoon) and June–August (monsoon) over the extreme monsoon year 2010 versus the regular periods of 2005–2009 by investigating precipitation mean, frequency, and intensity, as well as the diurnal and semidiurnal cycles. A noticeable bimodal variation during the 2010 season showed an increase in rainfall associated with anomalous atmospheric conditions, causing catastrophic floods in Pakistan.

Assessment of TRMM-based TMPA-3B42 and GSMaP precipitation products over India for the peak southwest monsoon season

Reliable information of rainfall and its variability at finer spatio-temporal scales over the Indian monsoon region is crucial for various applications in hydrology, meteorology, water resource management, and evaluation of high-resolution numerical model outputs. In this study, the Tropical Rainfall Measuring Mission (TRMM)-based high-resolution multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP) are evaluated against recently released gauge-based India Meteorological Department (IMD) gridded data set. Both the research (delayed mode) and near real-time rainfall products of TMPA-3B42 (V7 and RT) and GSMaP (MVK and NRT) are evaluated over India at a daily scale for the peak southwest monsoon months of July–August. Two study periods (2000–2010 and 2009–2013) are used to accommodate the analyses based on data availability. Comparison of TMPA-3B42 (V7 and RT) and GSMaP-MVK with IMD gauge-based data for 2000–2010 shows that these data sets are able to represent large-scale monsoon rainfall spatial features, but are having region-specific biases. The interannual variability of monsoon rainfall averaged over India is also reasonably captured. However in terms of biases, TMPA-3B42RT overestimates rainfall by about 21% and GSMaP-MVK underestimates by about 22% at all-India scale. In terms of continuous skill metrics, TMPA-3B42V7 is closer to the IMD gauge-based observations possibly due to gauge adjustments. Categorical verification against gauge-based observations shows that the satellite-based data sets have difficulties in rainfall detection primarily over the northern Jammu & Kashmir regions, northeast India, and southern peninsular India. At subregional scale within India, satellite rainfall estimates show rather lower skill score in rainfall identification and larger uncertainty in rainfall estimation over the northeast India. Comparison of near real-time products of TMPA and GSMaP for July–August months of 2009–2013 shows that TMPA-3B42RT overestimates and GSMaP-NRT underestimates rainfall over most parts of India. Even though TMPA-3B42 (V7 and RT) and GSMaP-MVK rainfall data sets show similar diurnal variations over the Indian monsoon region, TMPA-3B42RT (GSMaP-MVK) shows larger (smaller) magnitude in diurnal cycle as compared to TMPA-3B42V7. Results from different skill metrics suggest that both research and near real-time versions of TMPA products can be used with relatively higher confidence as compared to GSMaP over the Indian region during peak monsoon period.