TRMM) Multi-satellite Precipitation Analysis 3B42 Research Articles

Which Error Components in TRMM-Based Multisatellite Precipitation Estimates Reduce over Chinese Mainland after Official Bias Adjustments: Systematic or Random?

Abstract Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

Annual Cycle of Tropical and Subtropical Precipitation Estimated from TMPA 3B42 Dataset

The annual cycles of tropical and subtropical precipitation were estimated by parameterizing them using the inferential circular statistics approach. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) version 7 dataset which has spatial coverage from 50°S to 50°N and temporal coverage from 1998 to 2018 (21 years) was used to compute the monthly precipitation climatology data. Their annual cycles can be estimated using the bimodal von Mises distribution. The estimated parameters were found to sufficiently capture some interesting characteristics such as the non-uniformity and bimodality of precipitation rate associated with regions of convergence zones and subtropical highs. The difference in timings of maximum and minimum precipitation suggest that precipitation maxima over the continent were found to follow the position of the Sun at an average time lag of 1.2 months. Three examples of applying these parameters are given including the regional classification of precipitation which emphasizes on the bimodality the non-uniformity and the bimodality of their annual cycles, the evolution of the southern Intertropical Convergence Zone (ITCZ) in the Pacific Ocean which was found to propagate westward from 90°W in mid-March to 150°W in early-April at an estimated speed of 21.6 km/h, and the precipitation variability in the case of Paris in France in which the precipitation anomaly tend to decrease at a highly significant rate of −9.716 mm/month per decade (p value ≤0.01).

A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions

Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the similarity matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.

Spatial and temporal assessment of the extreme and daily precipitation of the Tropical Rainfall Measuring Mission satellite in Northeast Brazil

ABSTRACT This study assesses the ability of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 Version 7 (3B42 V7) product to estimate the occurrence and intensity of daily precipitation events, emphasizing extreme events, over a 16 year period (2000 to 2015) in the Northeast region of Brazil (NEB), which is characterized by different climatic conditions and high seasonal and spatial variability of rainfall. Statistical techniques such as the paired Student t-test, receiver operating characteristic curve (ROC), sensitivity analysis, cluster analysis and density graphing, along with descriptive statistics such as bias, standard deviation, mean square error and root mean square error were used. The results indicate that the quality of the satellite estimates depends on the seasonal period and the location and time scale in which the precipitation events occur. In general, the daily precipitation estimates of the satellite are statistically equal (p-value > 0.05) to those observed by rain gauges, except in the NEB east coast, where the observed precipitation values are associated with the occurrence of warm clouds. Regarding the extreme events, the satellite did not provide good estimates of extreme daily precipitation. However, rain accumulated in two days is enough to obtain better results for the quality of the extreme precipitation estimates coming from the satellite. Thus, the study provides users of the TMPA 3B42 V7 with prior knowledge regarding the quality of the estimates regarding the occurrence and intensity of precipitation events, especially extreme ones, by seasonal period in different areas of NEB.

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.

Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions: The upper Blue Nile Basin, Ethiopia

Accurate prediction of hydrological models requires accurate spatial and temporal distribution of rainfall. In developing countries, the network of observation stations for rainfall is sparse and unevenly distributed. Satellite-based products have the potential to overcome this shortcoming. The objective of this study is to compare the advantages and the limitation of commonly used high-resolution satellite rainfall products (Climate Forecast System Reanalysis (CFSR) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 version 7) as input to hydrological models as compared to sparsely and densely populated network of rain gauges. We used two (semi-distributed) hydrological models that performed well in the Ethiopian highlands: Hydrologiska Byråns Vattenbalansavdelning (HBV) and Parameter Efficient Distributed (PED). The rainfall products were tested in two watersheds: Gilgel Abay with a relatively dense network of rain gauge stations and Main Beles with a relatively scarce network, both are located in the Upper Blue Nile Basin. The results indicated that TMPA 3B42 was not be able to capture the gauged rainfall temporal variation in both watersheds and was not tested further. CFSR over predicted the rainfall pattern slightly. Both the gauged and the CFSR reanalysis data were able to reproduce the streamflow well for both models and both watershed when calibrated separately to the discharge data. Using the calibrated model parameters of gauged rainfall dataset together with the CFSR rainfall, the stream discharge for the Gilgel Abay was reproduced well but the discharge of the Main Beles was captured poorly partly because of the poor accuracy of the gauged rainfall dataset with none of the rainfall stations located inside the watershed. HBV model performed slightly better than the PED model, but the parameter values of the PED could be identified with the features of the landscape.

Performance Assessment of Multi-Source Weighted-Ensemble Precipitation (MSWEP) Product over India

Error characterization is vital for the advancement of precipitation algorithms, the evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) has been a benchmark for successive Global Precipitation Measurement (GPM) based products. This has given way to the evolution of many multi-satellite precipitation products. This study evaluates the performance of the newly released multi-satellite Multi-Source Weighted-Ensemble Precipitation (MSWEP) product, whose temporal variability was determined based on several data products including TMPA 3B42 RT. The evaluation was conducted over India with respect to the IMD-gauge-based rainfall for pre-monsoon, monsoon, and post monsoon seasons at daily scale for a 35-year (1979–2013) period. The rainfall climatology is examined over India and over four geographical extents within India known to be subject to uniform rainfall. The performance evaluation of rainfall time series was carried out. In addition to this, the performance of the product over different rainfall classes was evaluated along with the contribution of each class to the total rainfall. Further, seasonal evaluation of the MSWEP products was based on the categorical and volumetric indices from the contingency table. Upon evaluation it was observed that the MSWEP products show large errors in detecting the higher quantiles of rainfall (>75th and > 95th quantiles). The MSWEP precipitation product available at a 0.25° × 0.25° spatial resolution and daily temporal resolution matched well with the daily IMD rainfall over India. Overall results suggest that a suitable region and season-dependent bias correction is essential before its integration in hydrological applications. While the MSWEP was observed to perform well for daily rainfall, it suffered from poor detection capabilities for higher quantiles, making it unsuitable for the study of extremes.

Performance evaluation of TMPA version 7 estimates for precipitation and its extremes in Circum-Bohai-Sea region, China

Precipitation and its extremes are of significance for drought and flood warning and monitoring. This study evaluates the capability of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 V7 to detect rainfall events, especially extreme precipitation events, using gauge observations for the period 1998–2012 over Circum-Bohai-Sea region, a mid-altitude and semi-humid monsoon area. The results show that 3B42 V7 performs better at monthly and annual scales than at a daily scale. Spatially or seasonally, the rainfall pattern is more effectively captured by 3B42 V7 for the wet region or season than for the dry region or season. 3B42 V7 displays a positive relative bias in most areas, and the largest is situated in high latitude region, while negative relative bias is found at coastal regions. 3B42 V7 tends to overestimate at low and middle rainfall intensity (RI) ranges (RI <50 mm/day) but underestimate at high RI range (RI ≥50 mm/day). Overall, the total rainfall amount (PRETOT) and extreme precipitation amount (EPRETOT, above 95th percentile of daily rainfall) are slightly overestimated by 3B42 V7, while EPRETOT exhibits a lower correlation with observations than PRETOT does. The relative root mean square error (RMSE) are higher than 50 % relative to rain gauges for eight extreme precipitation indices except the maximum number of consecutive dry days (CDD), demonstrating that extreme precipitation estimates of 3B42 V7 are generally unreliable. The improvement of 3B42 V7 in capturing extreme precipitation events is anticipated through extensive efforts for its wide range of climate and hydrological applications. Overall, this study provides an evaluation of the quality of TMPA 3B42 V7 in estimating precipitation and its extremes in a mid-altitude and semi-humid monsoon region.

Evaluation of TMPA 3B42 Precipitation Estimates during the Passage of Tropical Cyclones over New Caledonia

This study evaluates the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 version 7 (V7) estimates of tropical cyclone (TC) rainfall over New Caledonia using the island rain gauge observations as the ground-truth reference. Several statistical measures and techniques are utilised to characterise the difference and similarity between TMPA and the gauge observations. The results show that TMPA has skill in representing the observed rainfall during the passage of TCs. TMPA overestimates light rainfall events and underestimates moderate to higher rainfall events. The skill deteriorates with increasing elevation, as underestimation by TMPA is greater at higher altitudes. The ability of TMPA also varies with TC intensity and distance from the TC centre, whereby it is more skilful for less intense TCs (category 1-2) and near the TC centre than in the outer rainbands. The ability of TMPA varies from case to case but a better performance is shown for TCs with a higher average rainfall. Finally, case studies of TC Vania (2011), TC Innis (2009), and TC Erica (2003) show that TMPA has the ability to represent the spatial distribution of the observed rainfall, but it tends to underestimate the higher rainfall events.

Assessment of satellite rainfall products over the Andean plateau

Nine satellite rainfall estimations (SREs) were evaluated for the first time over the South American Andean plateau watershed by comparison with rain gauge data acquired between 2005 and 2007. The comparisons were carried out at the annual, monthly and daily time steps. All SREs reproduce the salient pattern of the annual rain field, with a marked north–south gradient and a lighter east–west gradient. However, the intensity of the gradient differs among SREs: it is well marked in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 (TMPA-3B42), Precipitation Estimation from remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Global Satellite Mapping of Precipitation (GSMaP) products, and it is smoothed out in the Climate prediction center MORPHing (CMORPH) products. Another interesting difference among products is the contrast in rainfall amounts between the water surfaces (Lake Titicaca) and the surrounding land. Some products (TMPA-3B42, PERSIANN and GSMaP) show a contradictory rainfall deficit over Lake Titicaca, which may be due to the emissivity contrast between the lake and the surrounding lands and warm rain cloud processes. An analysis differentiating coastal Lake Titicaca from inland pixels confirmed this trend. The raw or Real Time (RT) products have strong biases over the study region. These biases are strongly positive for PERSIANN (above 90%), moderately positive for TMPA-3B42 (28%), strongly negative for CMORPH (−42%) and moderately negative for GSMaP (−18%). The biases are associated with a deformation of the rain rate frequency distribution: GSMaP underestimates the proportion of rainfall events for all rain rates; CMORPH overestimates the proportion of rain rates below 2mm day−1; and the other products tend to overestimate the proportion of moderate to high rain rates. These biases are greatly reduced by the gauge adjustment in the TMPA-3B42, PERSIANN and CMORPH products, whereas a negative bias becomes positive for GSMaP. TMPA-3B42 Adjusted (Adj) version 7 demonstrates the best overall agreement with gauges in terms of correlation, rain rate distribution and bias. However, PERSIANN-Adj's bias in the southern part of the domain is very low.

Probabilistic Precipitation Estimation with a Satellite Product

Satellite-based precipitation products have been shown to represent precipitation well over Nepal at monthly resolution, compared to ground-based stations. Here, we extend our analysis to the daily and subdaily timescales, which are relevant for mapping the hazards caused by storms as well as drought. We compared the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT product with individual stations and with the gridded APHRODITE product to evaluate its ability to retrieve different precipitation intensities. We find that 3B42RT, which is freely available in near real time, has reasonable correspondence with ground-based precipitation products on a daily timescale; rank correlation coefficients approach 0.6, almost as high as the retrospectively calibrated TMPA 3B42 product. We also find that higher-quality ground and satellite precipitation observations improve the correspondence between the two on the daily timescale, suggesting opportunities for improvement in satellite-based monitoring technology. Correlation of 3B42RT and 3B42 with station observations is lower on subdaily timescales, although the mean diurnal cycle of precipitation is roughly correct. We develop a probabilistic precipitation monitoring methodology that uses previous observations (climatology) as well as 3B42RT as input to generate daily precipitation accumulation probability distributions at each 0.25° x 0.25° grid cell in Nepal and surrounding areas. We quantify the information gain associated with using 3B42RT in the probabilistic model instead of relying only on climatology and show that the quantitative precipitation estimates produced by this model are well calibrated compared to APHRODITE.

Comparison of versions 6 and 7 3-hourly TRMM multi-satellite precipitation analysis (TMPA) research products

This paper examines differences between Version 6 (V6) and Version 7 (V7) 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multi-Satellite Precipitation Analysis (TMPA 3B42) research products in JJA (June, July and August) and DJF (December, January and February) over a 13-year period from 1998 to 2010 on a global scale. Different surface types and rain regimes are considered in the comparison. The study finds that more rain events are found in V7 than those in V6 in both JJA and DJF, especially over oceans. Overall, both versions show a good agreement in moderate and heavy rain regimes. High Pearson’s correlation coefficients are found in tropical rain band regions. Histograms of both versions are very similar; however higher frequencies of rain events are found in V7 in light rain regime, especially over oceans, than those in V6. For light rain, rainfall estimates in V6 are less than those in V7 over land and oceans in both seasons. For moderate rain, rainfall estimates in V6 are larger than those in V7 over land in most years. Over oceans, it is a mixed situation in which V6>V7 for some years and V6<V7 for the other years. For heavy rain, rainfall estimates in V6 are larger than those in V7 throughout all JJA and DJF seasons for both land and oceans, which is also shown in a case study. Large variance in the individual differences is found in light rain and less in heavy rain. No apparent trends are observed. For light rain, all statistics support that there is an uncertainty issue in both versions.

Evaluation of TMPA 3B42 daily precipitation estimates of tropical cyclone rainfall over Australia

Heavy rain from tropical cyclone (TC) landfall has extensive impacts on human life and society. Its estimation is subject to considerable uncertainty, especially in Australian tropical regions. In this study we evaluate the Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) 3B42 rainfall estimates in landfalling TCs over Australia. A high‐quality gauge‐based gridded rainfall product from the Australian Water Availability Project (AWAP) is utilized as reference data. The overall characteristics of TMPA 3B42 estimates are measured by mean rain rate, correlation coefficient, relative bias, relative root‐mean‐square error, and empirical orthogonal function analysis on both AWAP and TMPA 3B42. These comparisons show good correspondence over space and time between TMPA 3B42 and AWAP analysis for rainfall at TC landfall over Australia. The results also show that TMPA 3B42 generally overestimates TC rain for low rain rate but underestimates TC rain at high rain rate. TC intensity, location, terrain, and TC seasons all have impacts on TMPA 3B42's detection skill. For TC heavy rain, TMPA 3B42 shows better agreement with AWAP during more intense TCs (CAT3–5), in the eyewall as opposed to the rain bands, in the tropics as opposed to the subtropics, and in late TC seasons as opposed to early and peak TC seasons. Finally, a case study for TC Yasi (2011) is chosen to illustrate TMPA 3B42's ability to estimate TC landfall rainfall over Australia. Even though the performance of TMPA 3B42 can vary from case to case, TMPA 3B42 has a high correlation coefficient with AWAP and achieves good skill scores in most cases.