Performance comparison of GPM IMERG V07 with its predecessor V06 and its application in extreme precipitation clustering over Türkiye

In the summer of 2023, North China experienced a rare extreme precipitation storm due to Typhoons Doksuri and Khanun, leading to significant secondary disasters and highlighting the urgent need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products like Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission have great potential for enhancing forecasts, necessitating a quantitative evaluation before deployment. This study uses a dense rain gauge as a benchmark to assess the accuracy and capability of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products for the 2023 summer extreme precipitation in North China. These satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER) and IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), along with two gauge-corrected products, namely IMERG final run (IMERG_FR) and GSMaP gauge adjusted (GSMaP_Gauge). The results show that (1) GSMaP_MVK, IMERG_LR, and IMERG_FR effectively capture the space distribution of the extreme rainfall, with relatively high correlation coefficients (CCs) of approximately 0.77, 0.75, and 0.79. The IMERG_ER, GSMaP_NRT, and GSMaP_Gauge products exhibit a less accurate spatial pattern capture (CCs about 0.66, 0.73, and 0.67, respectively). Each of the six QPE products tends to underestimate rainfall (RBs < 0%). (2) The IMERG products surpass the corresponding GSMaP products in serial rainfall measurement. IMERG_LR demonstrates superior performance with the lowest root-mean-square error (RMSE) (about 0.38 mm), the highest CC (0.97), and less underestimation (RB about −6.37%). (3) The IMERG products at rainfall rates ≥ 30 mm/h, GSMaP_NRT and GSMaP_MVK products at rainfall rates ≥ 55 mm/h, and GSMaP_Gauge products at ≥ 40 mm/h showed marked limitations in event detection, with a near-zero probability of detection (POD) and a nearly 100% false alarm ratio (FAR). In this extreme precipitation event, caution is needed when using the IMERG and GSMaP products.

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science

Quantitative Precipitation Estimates (QPEs) from the Integrated Multisatellite Retrievals for GPM (IMERG) provide crucial information about the spatio-temporal distribution of precipitation in semiarid regions with complex orography, such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia is used to assess the performance of three IMERG products (Early, Late and Final) at different time scales, ranging from yearly to sub-daily periods. The analysis at a half-hourly scale also considered three different orographic features (valley, flat and ridgetop), diverse climatic conditions (BSk, Csa, Cf and Df) and five categories related to rainfall intensity (light, moderate, intense, very intense and torrential). While IMERG_E and IMERG_L overestimate precipitation, IMERG_F reduces the error at all temporal scales. However, the calibration to which a Final run is subjected causes underestimation regardless in some areas, such as the Pyrenees mountains. The proportion of false alarms is a problem for IMERG, especially during the summer, mainly associated with the detection of false precipitation in the form of light rainfall. At sub-daily scales, IMERG showed high bias and very low correlation values, indicating the remaining challenge for satellite sensors to estimate precipitation at high temporal resolution. This behaviour was more evident in flat areas and cold semi-arid climates, wherein overestimates of more than 30% were found. In contrast, rainfall classified as very heavy and torrential showed significant underestimates, higher than 80%, reflecting the inability of IMERG to detect extreme sub-daily precipitation events.

Evaluation of IMERG for GPM satellite-based precipitation products for extreme precipitation indices over Turkiye

Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm. Significance Statement Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.

The intensification of extreme precipitation has aggravated urban flood disasters, which makes timely and reliable precipitation information urgently needed. As the high-quality and widely used satellite precipitation products, Integrated Multi-satellitE Retrievals for GPM (IMERG), have not been well investigated in coastal urban agglomerations where damages from precipitation-related disasters are more severe. With precipitation measurements from local high-density gauge stations, this study evaluates three IMERG runs (IMERG ER, IMERG LR, and IMERG FR) in the southeast coastal urban region of China. The evaluation shows that the three IMERG products severely overestimate weak precipitation and underestimate heavy precipitation. Among the three runs, the post-corrected IMERG FR does not show a substantial improvement compared to the near-real-time IMERG ER and IMERG LR. The performance of IMERG varies depending on the precipitation pattern and intensity, with the best estimation ability occurring in the coastal urban region in summer and in the northern forests in winter. Due to the year-round urban effect on precipitation variability, IMERG cannot detect precipitation events well in the central high-density urban areas, and has its best detection ability on cultivated lands in summer and forests in winter. Within the urban agglomeration, IMERG shows a poorer performance in areas with higher urbanization levels. Thus, the IMERG products for coastal urban areas need considerable improvements, such as regionalized segmental corrections based on precipitation intensity and the adjustment of short-duration estimates by daily or sub-daily precipitation measurements.

Integrated Multi-satellite Retrievals for GPM (IMERG) data have been widely used to analyze extreme precipitation, but the data have never been validated for the Indonesian Maritime Continent (IMC). This study evaluated the capability of IMERG Early (E), Late (L), and Final (F) data to observe extreme rain in the IMC using the rain gauge data within five years (2016–2020). The capability of IMERG in the observation of the extreme rain index was evaluated using Kling–Gupta efficiency (KGE) matrices. The IMERG well captured climatologic characteristics of the index of annual total precipitation (PRCPTOT), number of wet days (R85p), number of very wet days (R95p), number of rainy days (R1mm), number of heavy rain days (R10mm), number of very heavy rain days (R20mm), consecutive dry days (CDD), and max 5-day precipitation (RX5day), indicated by KGE value &gt;0.4. Moderate performance (KGE = 0–0.4) was shown in the index of the amount of very extremely wet days (R99p), the number of extremely heavy precipitation days (R50mm), max 1-day precipitation (RX1day), and Simple Daily Intensity Index (SDII). Furthermore, low performance of IMERG (KGE &lt; 0) was observed in the consecutive wet days (CWDs) index. Of the 13 extreme rain indices evaluated, IMERG underestimated and overestimated precipitation of nine and four indexes, respectively. IMERG tends to overestimate precipitation of indexes related to low rainfall intensity (e.g., R1mm). The highest overestimation was observed in the CWD index, related to the overestimation of light rainfall and the high false alarm ratio (FAR) from the daily data. For all indices of extreme rain, IMERG showed good capability to observe extreme rain variability in the IMC. Overall, IMERG-L showed a better capability than IMERG-E and -F but with an insignificant difference. Thus, the data of IMERG-E and IMERG-L, with a more rapid latency than IMERG-F, have great potential to be used for extreme rain observation and flood modeling in the IMC.

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

Heavy precipitation events are recurrently occurring in Nepal, affecting lives and properties every year, especially in the summer monsoon season (i.e., June‐September). We investigated an extreme (heavy) precipitation event of August 2014 over the West Rapti River (WRR) Basin, Nepal. First, we forced a rainfall‐runoff model with ground‐based (gauge) hourly rainfall data of nine stations. Second, we validated against hourly water level at an outlet of the WRR Basin. This study then evaluated the performance of different satellite‐based rainfall estimates (SREs) in capturing an extreme precipitation event. We considered the use of half‐hourly data of Integrated Multi‐satellite Retrievals for GPM (IMERG) (Early, Late, and Final versions), spatial resolution (10 km), and hourly data of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), spatial resolution (25 km), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Cloud Classification System (PERSIANN‐CCS), spatial resolution (4 km). Also, we used 3 h data of Tropical Multi‐satellite Precipitation Analysis (TMPA) product real‐time (3B42RT), spatial resolution (25 km). In general, we find that all selected SREs depicted a similar pattern of extreme precipitation as shown by the gauge data on a daily scale. However, we find these products could not replicate precisely on a sub‐daily scale. Overall, IMERG and TMPA showed a better performance than PERSIANN and PERSIANN‐CCS. Finally, we corrected poor‐performed SREs with respect to gauge data and also filled data gaps of gauge rainfall using the information of good‐performed SREs. Our study reveals that there is a great challenge in local flood simulation employing SREs at high‐temporal resolution in Nepal.

Satellite precipitation products play an essential role in providing effective global or regional precipitation. However, there are still many uncertainties in the performance of satellite precipitation products, especially in extreme precipitation analysis. In this study, a Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) late run (LR) product was used to evaluate the rainstorms in the southern basin of China from 2015 to 2018. Three correction methods, multiple linear regression (MLR), artificial neural network (ANN), and geographically weighted regression (GWR), were used to get correction products to improve the precipitation performance. This study found that IMERG LR’s ability to characterize rainstorm events was limited, and there was a significant underestimation. The observation error and detection ability of IMERG LR decrease gradually from the southeast coast to the northwest inland. The error test shows that in the eastern coastal area (zone I and II), the central area (zone III), and the western inland area (zone IV and V), the optimal correction method is MLR, ANN, and GWR, respectively. The performance of three correction products is slightly better compared with the original product IMERG LR. From zone I to V, correlation coefficient (CC) and root mean square error (RMSE) show a decreasing trend. Zone II has the highest relative bias (RB), and the deviation is relatively large. The categorical indices of inland area performed better than coastal area. The correction product’s precipitation is slightly lower than the observed value from April to November with a mean error of 8.03%. The correction product’s precipitation was slightly higher than the observed values in other months, with an average error of 12.27%. The greater the observed precipitation, the higher the uncertainty of corrected precipitation result. The coefficient of variation showed that zone II had the highest uncertainty, and zone V had the lowest uncertainty. MLR had a high uncertainty with an average of 9.72%. The mean coefficient of variation of ANN and GWR is 7.74% and 7.29%, respectively. This study aims to generate a set of precipitation products with good accuracy through the IMERG LR evaluation and correction to support regional extreme precipitation research.

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

Extreme precipitation has significant impacts on human society and agriculture development under global climate change as well as severe effects on rice development. However, little research has been done on the characteristics of extreme precipitation in different rice growth stages. Taking the South China region as a case study, the characteristics of extreme daily precipitation in the early rice season (April–July) from 1960 to 2009 were investigated by using percentile method. Results indicated that extreme precipitation threshold increases since the tillering stage, and there is big difference between the first and the rest growth stages. Extreme precipitation is serious in the eastern part than in the western part of South China during the seeding and tillering stage, and reverse since the booting stage. Frequency of extreme precipitation increases in recent decades after the booting stage. Finally, flood risk regions form more easily in the coastal cities and the western part of South China in the 1990s and 2000s since the tillering stage.

In the summer of 2023, North China was hit by an exceptionally intense precipitation storm caused by Typhoons Doksuri and Khanun, resulting in significant secondary disasters and underscoring the critical need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products, such as Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission, show great merits for enhancing forecasts. This study uses a dense rain gauge network as a benchmark to evaluate the performance of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products during the 2023 summer extreme precipitation event in North China. The satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER), IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK),&amp;#160;as well as two gauge-corrected products&amp;#160;IMERG final run (IMERG_FR) and GSMaP gauge-adjusted (GSMaP_Gauge).&amp;#160;The results show that the satellite-based QPE products, particularly IMERG_LR and GSMaP_MVK, show good performance in capturing the spatial distribution and overall rainfall amounts&amp;#160;during&amp;#160;the extreme precipitation event. However, they have significant under-detect&amp;#160;high-intensity precipitation events in this region. The IMERG products generally outperform the GSMaP products, especially in terms of temporal rainfall measurement, but all products tend to underestimate rainfall.&amp;#160;At high rainfall rates, while the detection ability is high, the false alarm ratios are also significantly elevated&amp;#160;for all satellite-based QPE products. These findings highlight the need for further improvement&amp;#160;of satellite-based QPE products for more accurate and reliable rainfall estimation.

Abstract. In this study, we analyze extreme daily precipitation during the pre-industrial period from 1501 BCE to 1849 CE in simulations from the Community Earth System Model version 1.2.2. A peak-over-threshold (POT) extreme value analysis is employed to examine characteristics of extreme precipitation and to identify connections of extreme precipitation with the external forcing and with modes of internal variability. The POT analysis shows that extreme precipitation with similar statistical characteristics, i.e., the probability density distributions, tends to cluster spatially. There are differences in the distribution of extreme precipitation between the Pacific and Atlantic sectors and between the northern high and southern low latitudes. Extreme precipitation during the pre-industrial period is largely influenced by modes of internal variability, such as El Niño–Southern Oscillation (ENSO), the Pacific North American, and Pacific South American patterns, among others, and regional surface temperatures. In general, the modes of variability exhibit a statistically significant connection to extreme precipitation in the vicinity to their regions of action. The exception is ENSO, which shows more widespread influence on extreme precipitation across the Earth. In addition, the regions with which extreme precipitation is more associated, either by a mode of variability or by the regional surface temperature, are distinguished. Regional surface temperatures are associated with extreme precipitation over lands at the extratropical latitudes and over the tropical oceans. In other regions, the influence of modes of variability is still dominant. Effects of the changes in the orbital parameters on extreme precipitation are rather weak compared to those of the modes of internal variability and of the regional surface temperatures. Still, some regions in central Africa, southern Asia, and the tropical Atlantic ocean show statistically significant connections between extreme precipitation and orbital forcing, implying that in these regions, extreme precipitation has increased linearly during the 3351-year pre-industrial period. Tropical volcanic eruptions affect extreme precipitation more clearly in the short term up to a few years, altering both the intensity and frequency of extreme precipitation. However, more apparent changes are found in the frequency than the intensity of extreme precipitation. After eruptions, the return periods of extreme precipitation increase over the extratropical regions and the tropical Pacific, while a decrease is found in other regions. The post-eruption changes in the frequency of extreme precipitation are associated with ENSO, which itself is influenced by tropical eruptions. Overall, the results show that climate simulations are useful to complement the information on pre-industrial extreme precipitation, as they elucidate statistical characteristics and long-term connections of extreme events with natural variability.

This paper evaluates the use of precipitation forecasts from a numerical weather prediction (NWP) model for near-real-time satellite precipitation adjustment based on 81 flood-inducing heavy precipitation events in seven mountainous regions over the conterminous United States. The study is facilitated by the National Center for Atmospheric Research (NCAR) real-time ensemble forecasts (called model), the Integrated Multi-satellitE Retrievals for GPM (IMERG) near-real-time precipitation product (called raw IMERG) and the Stage IV multi-radar/multi-sensor precipitation product (called Stage IV) used as a reference. We evaluated four precipitation datasets (the model forecasts, raw IMERG, gauge-adjusted IMERG and model-adjusted IMERG) through comparisons against Stage IV at six-hourly and event length scales. The raw IMERG product consistently underestimated heavy precipitation in all study regions, while the domain average rainfall magnitudes exhibited by the model were fairly accurate. The model exhibited error in the locations of intense precipitation over inland regions, however, while the IMERG product generally showed correct spatial precipitation patterns. Overall, the model-adjusted IMERG product performed best over inland regions by taking advantage of the more accurate rainfall magnitude from NWP and the spatial distribution from IMERG. In coastal regions, although model-based adjustment effectively improved the performance of the raw IMERG product, the model forecast performed even better. The IMERG product could benefit from gauge-based adjustment, as well, but the improvement from model-based adjustment was consistently more significant.