GPM IMERG Research Articles

Convective Cloud Classification Model for Reconstruction of Heavy Rain That Triggers the Flood and Landslide in Parapat, North Sumatera

Rainfall in Parapat as a part of Lake Toba, North Sumatera and a super-priority tourist destination in Indonesia on May, 13 2021 causes flood, it exceeds Meteorologi, Climatologi and Geophysics Agency’s threshold about intensity of extreme condition. It is related to dynamic of weather’s parameter, especially with convection process and clouds. The landslide material was very closed the road so that the SiantarParapat access was temporarily closed down. Heavy rains that have occurred since noon are said to be the cause of the floods and landslides that occurred at night. This study aims to describe the distribution of rain in Parapat and surrounding areas during floods and landslides. The data used are measured rainfall at the nearest AWS (Automatic Weather Station), and rainfall estimation data from GPM IMERG (Global Precipitation Measurement – The Integrated Multi-SatellitE Retrievals). The results of the study show that convective cloud classification model can be identify the reconstruction of heavy rain that trigger the flood and landslide in parapet. There has been rain with moderate to heavy intensity since 12 May 2021. Heavy rains that have occurred in the last few days have caused soil conditions to become saturated, resulting in landslides. Cloud classification shows the presence of Cumulonimbus cloud clusters that grow and develop around the Lake Toba area before and during heavy rains.

Bias correction of GPM IMERG Early Run daily precipitation product using near real-time CPC global measurements

This study focused on improving the performance of the near real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Early Run (IMERG-E) product based on a newly developed bias-correction scheme, LSCDF. The LSCDF was established by integrating the mean-based Linear Scaling (LS) and quantile-mapping Cumulative Distribution Function (CDF) matching approaches. The daily updating gauge-based precipitation data from the Climate Prediction Center (CPC) unified analysis were used as the benchmark for bias correction. The IMERG-E bias-corrected precipitation data were evaluated against the daily ground measurements from 807 meteorological stations across mainland China and compared with the raw IMERG-E and IMERG Final Run (IMERG-F; research-level with gauge calibration) retrievals. Evaluation results for the period 2015 to 2017 showed that the LSCDF method effectively improves near real-time IMERG-E precipitation estimates at the daily scale. The bias-corrected IMERG-E precipitation estimates were in significantly better agreement with ground measurements than the original IMERG-E at the point-daily resolutions, with the correlation coefficient increasing from 0.66 to 0.77, relative bias decreasing from 11.1% to −3.1%, and root-mean-square error dropping from 6 mm/day to 4.39 mm/day. In addition, the bias-corrected IMERG-E was apparently superior to IMERG-E in detecting precipitation events at various intensity levels including small, light, moderate, and heavy precipitation. Although IMERG-E tended to significantly underestimate or overestimate precipitation during three typical typhoons, the bias-corrected IMERG-E can match the rainfall spatial distributions well, demonstrating a considerable capability in capturing the extreme precipitation. Generally, the bias-corrected IMERG-E featured a substantial improvement over the original IMERG-E, and it even exhibited a more satisfactory overall performance than the research-level gauge-calibrated IMERG-F product. Our study demonstrates that the bias-correction method LSCDF can improve the accuracy of satellite precipitation products to benefit the near real-time application of satellite products for natural hazards.

Spatiotemporal Evaluation and Estimation of Precipitation of Multi-Source Precipitation Products in Arid Areas of Northwest China—A Case Study of Tianshan Mountains

In the arid areas of Northwest China, especially in the Tianshan Mountains, the scarcity of meteorological stations has brought some challenges in collecting accurate information to describe the spatial distribution of precipitation. In this study, the applicability of TRMM3B42, GPM IMERG, and MSWEP V2.2 in different regions of Tianshan Mountain is comprehensively evaluated by using ten statistical indicators, three classification indicators, and variation coefficients at different time–space scales, and the mechanism of accuracy difference of precipitation products is discussed. The results show that: (1) On the annual and monthly scales, the correlation between GPM and measured precipitation is the highest, and the ability of three precipitation products to capture precipitation in the wet season is stronger than that in the dry season; (2) On the daily scale, TRMM has the highest ability to estimate the frequency of light rain events, and MSWEP has the highest ability to monitor extreme precipitation events; (3) On the spatial scale, GPM has the highest fitting degree with the spatial distribution of precipitation in Tianshan Mountains, MSWEP is the closest to the precipitation differentiation pattern in Tianshan Mountains; (4) The three satellite products generally perform best in low and middle longitude regions and middle elevation regions. This study provides a reference for the selection of grid precipitation datasets for hydrometeorological simulation in northwest arid areas and also provides a basis for multi-source data assimilation and fusion.

Hydro Statistical Assessment of TRMM and GPM Precipitation Products against Ground Precipitation over a Mediterranean Mountainous Watershed (in the Moroccan High Atlas)

The tropical Rainfall Measuring Mission TRMM 3B42 V7 product and its successor, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM IMERG high-resolution product GPM IMERG V5, have been validated against rain gauges precipitation in an arid mountainous basin where ground-based observations of precipitation are sparse, or spatially undistributed. This paper aims to evaluate hydro-statically the performances of the TRMM 3B42 V7 and GPM IMERG V05 satellite precipitations products SPPs, at multiple temporal scales, from 2014 to 2017. SPPs are compared with the gauge station and show good results for both statistical and contingency metrics with notable values R > 0.94. Moreover, the rainfall-runoff events implemented on the hydrological model were performed at 3-hourly time steps and showed satisfactory results based on the obtained Nash–Sutcliffe criteria ranging from 94.50% to 57.50%, and from 89.3% to 51.2%, respectively. The TRMM product tends to underestimate and not capture extreme precipitation events. In contrast, the GPM product can identify the variability of precipitation at small time steps, although a slight underestimation in the detection of extreme events can be corrected during the validation steps. The proposed method is an interesting approach for solving the problem of insufficient observed data in the Mediterranean regions.

An analysis of the characteristics of precipitation in the Northeast passage and its relationship with sea ice

Precipitation is an important part of the atmospheric circulation in the Arctic and is of great significance to the energy budget and hydrological characteristics of the Arctic region. The distribution of precipitation affects the exchange of energy, which then affects the Arctic sea ice indirectly. Arctic precipitation impacts the sea surface albedo, which leads to changes in the sea ice concentration (SIC) and the energy exchange between the sea, ice, and air. In this study, GPM IMERG precipitation data, which have a spatial resolution of 0.1°, were used to analyze the characteristics of precipitation in the Northeast Passage (NEP) from May to December during the period 2011–2020. This analysis of the amount of precipitation and its distribution were performed for the Barents Sea, Kara Sea, Laptev Sea, and East Siberian Sea. The relationship between precipitation and sea ice was also explored. The results show that, during the study period, the average precipitation over the Barents Sea from May to December was 57–561 mm/year and that this area had the highest precipitation in the NEP. For the Kara Sea, the average precipitation for May to December was 50–386 mm/year and for the East Siberian Sea and the Laptev Sea it was 48–303 mm/year and 53–177 mm/year, respectively. For the NEP as a whole, September was found to be the month with the highest average precipitation. An analysis of the correlation between the precipitation and the SIC gave a correlation coefficient of −0.792 for the study period and showed that there is a 15-day delay between the precipitation increase and the decrease in SIC. The analysis of the precipitation data in these areas thus showed that precipitation is related to SIC and is of great importance to understanding and predicting the navigable capacity of the NEP.

Global Tropical Precipitation Relationships to Free-Tropospheric Water Vapor Using Radio Occultations

Abstract The transition to deep convection and associated precipitation is often studied in relationship to the associated column water vapor owing to the wide availability of these data from various ground or satellite-based products. Based on radiosonde and ground-based global navigation satellite system (GNSS) data examined at limited locations and model comparison studies, water vapor at different vertical levels is conjectured to have different relationships to convective intensity. Here, the relationship between precipitation and water vapor in different free-tropospheric layers is investigated using globally distributed GNSS radio occultation (RO) temperature and moisture profiles collocated with GPM IMERG precipitation across the tropical latitudes. A key feature of the RO measurement is its ability to directly sense in and near regions of heavy precipitation and clouds. Sharp pickups (i.e., sudden increases) of conditionally averaged precipitation as a function of water vapor in different tropospheric layers are noted for a variety of tropical ocean and land regions. The layer-integrated water vapor value at which this pickup occurs has a dependence on temperature that is more complex than constant RH, with larger subsaturation at warmer temperatures. These relationships of precipitation to its thermodynamic environment for different layers can provide a baseline for comparison with climate model simulations of the convective onset. Furthermore, vertical profiles before, during, and after convection are consistent with the hypothesis that the lower troposphere plays a causal role in the onset of convection, while the upper troposphere is moistened by detrainment from convection.

Remote Sensing for Development of Rainfall Intensity–Duration–Frequency Curves at Ungauged Locations of Yangon, Myanmar

This study aims to develop the intensity–duration–frequency (IDF) curves for Yangon, the economic center of Myanmar, using four satellite precipitation datasets, namely GPM IMERG, TRMM, GSMaP_NRT, and GSMaP_GC. Different probability distribution functions were used to fit the annual rainfall maximum series to determine the best-fit distribution. The estimated parameters of the best-fit distribution were used to fit the rainfall intensities of 2, 5, 10, 25, 50, and 100-year return periods for generating IDF curves using the Sherman equation. The IDF curves were bias-corrected based on the daily rainfall data available only at a location in Yangon. The bias correction factors were then used to estimate IDF curves from satellite rainfall at ungauged locations of Yangon. The results showed that the Generalized Extreme Value Distribution best fit the hourly rainfall distribution of satellite data. Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is the most suitable for constructing Yangon’s IDF curves. The bias-corrected IDF curve generated at four locations of greater Yangon indicates higher rainfall intensity at the coastal stations than the inland stations. The methodology presented in this study can be used to derive IDF curves for any location in Myanmar.

Comparison of flow simulations with sub-daily and daily GPM IMERG products over a transboundary Chenab River catchment

Abstract This study proposes the assessment of SWAT model simulations, with the provision of satellite precipitation products (SPPs), in a transboundary/large catchment. Three latest sub-daily/half-hourly (HH) and daily (D) SPPs, i.e., ‘IMERG-E’, ‘IMERG-L’, and ‘IMERG-F’, were evaluated for daily and monthly flow simulations. The study revealed that monthly flow simulation performance is better than daily flow simulation in all sub-daily and daily SPPs-based models. Results depict that IMERG-HHF and IMERG-DF yield the best performance among the other latency levels of SPPs. The IMERG-HHF model has a reasonably higher daily correlation coefficient (R) and lower daily root-mean-square error (RMSE) than IMERG-DF. IMERG-HHF displays the lowest percent bias (PBIAS) values of 15.4 and 2.4 for daily and monthly flow validation, respectively. It also represents relatively higher values of coefficient of determination (R2) and Nash–Sutcliffe Efficiency (NSE) than any other model, i.e., R2=0.66 and NSE=0.63 for daily model validation and R2=0.84 and NSE=0.82 for monthly model validation. Moreover, the sub-daily IMERG model outperformed the daily IMERG model for all calibration and validation scenarios. The IMERG-DL model demonstrates poor performance in all of the SPPs, in daily and monthly validation, with low R2 (0.63 (dval) and 0.81 (mval)), low NSE (0.50 (dval) and 0.67 (mval)), and high PBIAS (31 (dval) and 26.6 (mval)). Additionally, the IMERG-HHE model outperformed IMERG-HHL.

Can GPM IMERG Capture Extreme Precipitation in North China Plain?

Extreme precipitation events (EPE) often cause catastrophic floods accompanied by serious economic losses and casualties. The latest version (V06) of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) provides global satellite precipitation data from 2000 at a higher spatiotemporal resolution with improved quality. It is scientifically and practically important to assess the accuracy of the IMERG V06 in capturing extreme precipitation. This study evaluates the two widely used products of IMERG during 2000–2018, i.e., IMERG late run (IMERG-L) and IMERG final run (IMERG-F), in the densely populated and flood-prone North China Plain. The accuracy of the IMERG V06 is evaluated with ground measurements from rain gauge stations at multiple scales (hourly, daily, and seasonally). A novel target tracking method is introduced to extract three-dimensional (3D) extreme precipitation events, and the near-real-time uncalibrated PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System) and GSMAP (Global Satellite Mapping of Precipitation) satellite data are added to further evaluate IMERG’s performance during extreme precipitation. Finally, for flash flood events induced by extreme rainfall in the Hebei Province from 15 to 23 July 2016, the accuracy of capturing the event with IMERG-F and IMERG-L was verified. Results reveal that IMERG-F is better than IMERG-L at all investigated scales (hourly, daily, and seasonally), but the difference between the two products is less at higher time resolutions. Both products manifest decreased performance when capturing 3D extreme precipitation events, and comparatively, IMERG-F performs better than IMERG-L. IMERG-F exhibits a distinct discontinuity in extreme precipitation thresholds between land and ocean, which is a limitation of IMERG-F not documented in previous studies. Moreover, IMERG-L and IMERG-F are comparable at an hourly scale for some metrics, which is beyond the expectation that IMERG-F is notably better than IMERG-L. This study provides a scientific basis for the performance of satellite precipitation products and contributes to guiding users when applying global precipitation products.

Performance evaluation, error decomposition and Tree-based Machine Learning error correction of GPM IMERG and TRMM 3B42 products in the Three Gorges Reservoir Area

Satellite precipitation products (SPPs) have excellent development prospects in hydrometeorology research for their high spatial-temporal resolution. Understanding the performance of SPPs is essential before their application in estimating multiple processes. This paper evaluated the accuracy and error composition of GPM IMERG Final-run V06 and TRMM 3B42V7 in the Three Gorges Reservoir Area (TGRA) based on the China Gauge-based Daily Precipitation Analysis dataset (CGDPA) during 2001–2019. Afterwards, the precipitation correction by merging satellite and gauge observation precipitation was conducted with three Tree-based Machine Learning models, Decision Tree (DT), Adaptive Boosting Decision Trees (Adaboost), and Random Forest (RF). The results show that: (1) Two SPPs can well reflect the spatial pattern and in-year variation of precipitation in the TGRA. However, they are limited in estimating precipitation amount for seriously underestimated the light rain or overestimated torrential rain. Under the influence of climate and variety terrains, the SPPs accuracy in the middle-elevation relief mountains of downstream is significantly higher than that in the low-elevation relief mountains of upstream. Moreover, that on the monthly and annual scales are higher than on the daily scale. Comparing two SPPs, the area proportions of IMERG-F better than 3B42V7 are 64.0%, 86.0%, and 81.6% on daily, monthly and annual scales, respectively. (2) Both the systematic error components of two SPPs raise with the increase of precipitation amount, and the error spatial pattern of 3B42V7 is more affected by precipitation. The random error component of light rain and daily precipitation is higher than other grades and scales. Despite this, the random error component decreases significantly when daily precipitation is accumulated. (3) The three primary factors affecting error correction of SPPs are precipitation amount, longitude, and DEM in TGRA. Among the three Tree-based methods, the RF showed the best error correction effect, and it can significantly improve the monthly and annual precipitation accuracy and better reproduce probability density function (PDF). Cumulative precipitation can reduce the random error component of daily precipitation and have high correction accuracy when cumulative time exceeds 5-days.

The Role of Mesoscale Convective Systems in Precipitation in the Tibetan Plateau Region

AbstractMesoscale convective systems (MCSs) have been identified as an important source of precipitation in the Tibetan Plateau (TP) region. However, the characteristics and structure of MCS‐induced precipitation are not well understood in this location. Infrared (IR) satellite imagery has been used for MCS tracking, but cirrus clouds or cold surfaces can lead to false MCS classification over mountain regions. Here, we combine brightness temperatures from IR imagery with satellite precipitation estimates from GPM IMERG and track MCSs over the TP, at the boundary of the TP (TPB), and in the surrounding lower‐elevation plains (LE), between 2000 and 2019. In most parts of LE and TPB, MCSs produced 50%–80% of the total summer precipitation (60%–90% of summer heavy precipitation), whereas MCSs over the TP account for below 10% of the total summer precipitation (10%–30% of summer heavy precipitation). Our results also show that MCSs that produce the largest amounts of heavy precipitation are characterized by longevity and large extents rather than by high intensities. These are mainly located in the populous areas south and east of the TP. A tracking of meso‐β convective systems over the TP shows that small‐scale convection makes a large contribution to total and heavy precipitation. This suggests that more localized convective systems are important for the regional water cycle over the higher terrain and highlights the importance of convective‐scale modeling to improve our understanding of precipitation dynamics in the TP region.

Evaluation of the Performance of Multi-Source Precipitation Data in Southwest China

The number of precipitation products at the global scale has increased rapidly, and the accuracy of these products directly affects the accuracy of hydro-meteorological simulation and forecast. Therefore, the applicability of these precipitation products should be comprehensively evaluated to improve their application in hydrometeorology. This paper evaluated the performances of six widely used precipitation products in southwest China by quantitative assessment and contingency assessment. The precipitation products were Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 version 7 (TRMM 3B42 V7), Global Satellite Mapping of Precipitation (GSMaP MVK), Integrated Multi-satellitE Retrievals for GPM final run (GPM IMERG Final), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network—Climate Data Record (PERSIANN-CDR), Climate Hazards Infrared Precipitation with Stations version 2.0 (CHIRPS V2.0), and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). From the above six products, the daily-scale precipitation data from 2001 to 2019 were chosen to compare with the measured data of the rain gauge, and the data from the gauges were classified by river basin and elevation. All precipitation products and measured data were evaluated by statistical indicators. Results showed that (1) GPM IMERG Final and CHIRPS V2.0 performed well in the Yarlung Zangbo River (YZ) basin, while GPM IMERG Final and GLDAS V2.0 performed well in the Lantsang River (LS), Nujiang River (NJ), Yangtze River (YT), and Yellow River (YL) basins; (2) in the upper and middle reaches of the YZ basin, GPM IMERG Final and CHIRPS V2.0 were outstanding in all evaluated products; downstream of the YZ basin, all six products performed well; and upstream of the LS and NJ, GPM IMERG Final, TRMM 3B42 V7, CHIRPS V2.0, and GLDAS V2.0 can be recommended as a substitute for measured data; and (3) GPM IMERG Final and GLDAS V2.0 can be seen as substitutes for measured data when elevation is below 4000 m. GPM IMERG Final and CHIRPS V2.0 were recommended when elevation is above 4000 m. This study provides a reference for data selection of hydro-meteorological simulation and forecast in southwest China and also provides a basis for multi-source data assimilation and fusion.

Simulation of atmospheric dynamics during heavy rain events on Nias Island using WRF model and Himawari-8 satellite data

Nias is the largest island in the Indian Ocean west of Sumatra. The geographical condition of Nias Island which is a small island surrounded by waters causes strong weather dynamics and high intensity of rainfall. This study was conducted to determine changes in weather dynamics conditions during high rainfall on Nias Island, including the parameters of the vertical profile of air humidity, vorticity, divergence, vertical velocity, reflectivity; and cloud top temperatures. The simulation was carried out on 4 days of heavy rain in 2020, each representing each season period, namely 7 February 2020 for December-January-February (DJF), 30 April 2020 for March-April-May (MAM), 3 August 2020 for June-July-August (JJA), and 8 October 2020 for September-October-November (SON). The data used are Final Analysis Data (FNL) with a spatial resolution of 1°x1° as input data for the Weather Research and Forecast (WRF) model, IR1 data (band#13 – 10.4µm) Himawari-8 satellite, and observational rainfall data from the Binaka and Global Meteorological Stations. Precipitation Measurement – The Integrated Multi-Satellite Retrievals for GPM (GPM IMERG) with a spatial resolution of 0.1°x0.1°. The results showed that the presence of Cumulonimbus convective clouds caused heavy rain, with cloud top temperatures reaching -60°C to -80°C. The relatively humid atmosphere, accompanied by the convection mechanism that occurs, causes the convective activity on Nias Island to be quite intense.

Evaluation of Tropical Rainfall Measuring Mission, Integrated Multi‐satellite Retrievals for GPM, Climate Hazards Centre InfraRed Precipitation with Station data, and European Centre for Medium‐Range Weather Forecasts Reanalysis v5 data in estimating precipitation and capturing meteorological droughts over Iran

AbstractSatellite remote‐sensing products with high spatial and temporal resolution are viable sources of precipitation information, especially for data‐sparse and remote regions. The aim of this study is to examine the performance of four precipitation products including Integrated Multi‐satellite Retrievals for GPM (GPM IMERG), Tropical Rainfall Measuring Mission (TRMM 3B43), Climate Hazards Centre InfraRed Precipitation with Station data (CHIRPS), and European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) in estimating precipitation and capturing meteorological droughts over Iran for the time span from 2001 to 2019. For this aim, a ground‐based gridded precipitation dataset was constructed over the country using a dense network of quality‐controlled rain gauges as a reference dataset. Different statistical metrics including the correlation coefficient (CC), the bias, the relative bias, and the root mean square error (RMSE) were applied to evaluate the performance of the products. The results suggest that GPM IMERG and TRMM 3B43 outperform CHIRPS and ERA5 in capturing the spatial distribution of precipitation and meteorological drought events across the country. The estimates of precipitation from the four products are seasonally influenced, with the least accurate precipitation estimates during summer season over the southern shores of the Caspian Sea. The GPM IMERG and TRMM 3B43, with higher CC and lower RMSE, show better performance in detecting drought events at both short and long time scales while the CHIRPS demonstrates the least accuracy. Spatially, all of the products show the best performance in identifying drought events over western and southwestern regions.

Assessment of Satellite-based Precipitation Products Performance over the Hyper-arid Climate of Kuwait

AbstractPrecipitation is a complex natural parameter that is essential for water and environmental systems. Due to its variability on the spatial and temporal scales, satellite-based precipitation products (SPPs) have arisen interest in hydrology and meteorology applications. This study measures the performance of six high resolutions SPPs (GPM IMERG products (IMERG-E, IMERG-L, and IMERG-F), TMPA products (3B42 V7, 3B42RT V7), and PERSIANN product) in producing the observed precipitation over a hyper-arid climate, water scarce region for the period 2013-2018. It also evaluates their performance dependency on the aggregation time-step and topographic elevations. According to a number of continuous and categorical evaluation metrics: (a) SPPs overestimate the observed daily annual and seasonal precipitation, particularly with near real-time products, (b) all SPPs estimates depict correlation ranging from 0.68 to 0.84 with the annual and seasonal precipitation and weak correlations in dry season, and (c) their ability to detect rain/no-rain events is measured by Peirce Skill Score (PSS), ranging from 0.73 to 0.92 across annual and seasonal scales, whereas 3B42RT V7 reproduces lower PSSs. Furthermore, the study finds that aggregation to a monthly time-step improves only SPPs correlations. The performance of near real-time products shows significant dependency on elevations, especially with 3B42RT V7 that shows low skills at coastlands. The TMPA products ability to detect rain/no-rain events dramatically drops from highlands to coastlands, with low skills to generate observed no/tiny and light precipitation classes. The study addresses an adequate ability of IMERG-F and PERSIANN to be utilized in water and environmental studies over hyper-arid climate regions, with highlighting for the superiority of IMERG-F.

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in the groundwater basin of the Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail. CM (CMORPH) has larger underestimation compared to other satellite-based rainfall assessments. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) has the lowest. CM has a good performance to detect no rain, while IMR (GPM IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM), especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall assessments have large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall than to merge both IMR and TR together.

Improved Rainfall Data in the Philippines through Concurrent Use of GPM IMERG and Ground-Based Measurements

The availability of accurate and reliable rainfall data that are applicable to various phenomenological, climatological, and modeling studies is important, especially in the Philippines, which is considered to be highly vulnerable to natural hazards and a changing climate. The presented strategy involved constructing a dataset consisting of synoptic data, automatic rain gauge (ARG) measurements, and satellite data that are co-registered, consistent, and formatted in the same manner. Although sparse in number, the synoptic stations provide the most accurate rainfall information and were used as the baseline for creating the dataset. The ARGs that are within a distance of 1 km to the synoptic stations were used to determine the correction factors needed to make the synoptic and ARG data consistent. Subsequently, the corrected ARGs were used to make the satellite IMERG data consistent with both ARG and synoptic data. In case of the latter, only IMERG pixels with at least 10 ARGs within the relatively large footprint of the satellite sensor were used in estimating the required correction parameters derived from a combination of a power transform and linear regression correction techniques. The final results show good agreement of synoptic and corrected ARG data with correlation coefficients of 0.94 and 0.97 for the 10 day and monthly data, respectively, and improvement in the linear regression slope from 0.67 to 0.90 for 10 day data, and 0.70 to 0.94 for monthly data. In addition, the corrected ARG data agree well with the corrected IMERG data, with correlation coefficients of 0.88 and 0.93 for the 10 day and monthly data, respectively, and an improvement in slope from 0.66 to 0.87 for 10 day data, and 0.74 to 0.99 for monthly data. The merit of using a combined dataset is illustrated through comparative analyses of the IMERG data and spatially interpolated synoptic and ARG data. The results show general agreements in spatial patterns of rainfall across the datasets, especially in areas where in situ measurements are recorded. The observed discrepancy when ground data is limited emphasizes the need for satellite IMERG data to obtain the true spatial patterns of rainfall distribution.

Impact of the Madden–Julian Oscillation on extreme precipitation over the western Maritime Continent and Southeast Asia

AbstractThe western Maritime Continent (MC) and Southeast Asia lie at the heart of the largest area of high precipitation on Earth. Extreme precipitation is one of the major high‐impact weather events to affect the population of over 500 million in this region. The deep convection associated with this extreme precipitation is difficult to forecast, even with modern high‐resolution numerical weather prediction with explicit convection. However, larger‐scale organised convective systems, such as the Madden–Julian Oscillation (MJO), can be skilfully predicted to 3–5 weeks lead time. The MJO has a well‐known precipitation signal, and it is likely that it also modulates extreme precipitation. Here, the extreme precipitation signal of the MJO is analysed in detail for the western MC and Southeast Asia using 19 years of high‐resolution Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) data. The probability of experiencing extreme precipitation increases robustly by a factor of two, and decreases by a factor of half, dependent on location and the phase of the MJO. The spatial pattern of these changes in extreme precipitation does not describe a smooth eastward propagation, but shows rapid variation over short distances, tied to the complex distribution of land and sea within the archipelago. There is also a seasonal dependence of this MJO modulation in some locations. A more detailed analysis of the effect of the MJO on extreme precipitation is presented for the major cities in the region. Extreme precipitation days over the MC are generally linked with an amplification of the diurnal cycle. However, although an active MJO increases the frequency of extreme precipitation days and therefore an amplified diurnal cycle, there was no further amplification of the diurnal cycle in the active MJO, compared with extreme precipitation days during non‐active MJO periods.

Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting?

Predicting rainfall-induced landslides hinges on the quality of the rainfall product. Satellite rainfall estimates or rainfall reanalyses aid in studying landslide occurrences especially in ungauged areas, or in the absence of ground-based rainfall radars. Quality of these rainfall estimates is critical; hence, they are commonly crosschecked with their ground-based counterparts. Beyond their temporal precision compared to ground-based observations, we investigate whether these rainfall estimates are adequate for hindcasting landslides, which particularly requires accurate representation of spatial variability of rainfall. We developed a logistic regression model to hindcast rainfall-induced landslides in two sites in Japan. The model contains only a few topographic and geologic predictors to leave room for different rainfall products to improve the model as additional predictors. By changing the input rainfall product, we compared GPM IMERG and ERA5 rainfall estimates with ground radar–based rainfall data. Our findings emphasize that there is a lot of room for improvement of spatiotemporal prediction of landslides, as shown by a strong performance increase of the models with the benchmark radar data attaining 95% diagnostic performance accuracy. Yet, this improvement is not met by global rainfall products which still face challenges in reliably capturing spatiotemporal patterns of precipitation events.

Assessment of GPM IMERG Satellite Precipitation Estimation under Complex Climatic and Topographic Conditions

Satellite precipitation estimation provides crucial information for those places lacking rainfall observations from ground–based sensors, especially in terrestrial or marine areas with complex climatic or topographic conditions. This is the case over much of Western China, including Upper and Middle Lancang River Basin (UMLRB), an extremely important transnational river system in Asia (the Lancang–Mekong River Basin) with complex climate and topography that has limited long–term precipitation records and high–elevation data, and no operational weather radars. In this study, we evaluated three GPM IMERG satellite precipitation estimation (IMERG E, IMERG L and IMERG F) over UMLRB in terms of multi–year average precipitation distribution, amplitude consistency, occurrence consistency, and elevation–dependence in both dry and wet seasons. Results demonstrated that monsoon and solid precipitation mainly affected amplitude consistency of precipitation, aerosol affected occurrence consistency of precipitation, and topography and wind–induced errors affected elevation dependence. The amplitude and occurrence consistency of precipitation were best in wet seasons in the Climate Transition Zone and worst in dry seasons in the same zone. Regardless of the elevation–dependence of amplitude or occurrence in dry and wet seasons, the dry season in the Alpine Canyon Area was most positively dependent and most significant. More significant elevation–dependence was correlated with worse IMERG performance. The Local Weighted Regression (LOWERG) model showed a nonlinear relationship between precipitation and elevation in both seasons. The amplitude consistency and occurrence consistency of both seasons worsened with increasing precipitation intensity and was worst for extreme precipitation cases. IMERG F had great potential for application to hydroclimatic research and water resources assessment in the study area. Further research should assess how the dependence of IMERG’s spatial performance on climate and topography could guide improvements in global precipitation assessment algorithms and the study of mountain landslides, floods, and other natural disasters during the monsoon period.