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Diurnal phase of mean unconditional precipitation over the region shown in Figure 1 from PR and DPR at 0.5 ° × 0.5 ° resolution (a), TMI and GMI at 0.5 ° × 0.5 ° resolution (b), IMERG at 0.5 ° × 0.5 ° resolution (c), and ground stations (d). The diurnal phase of mean unconditional precipitation at each ground station is also indicated by the squares in panels (a–c). DPR, Dual‐frequency Precipitation Radar; GMI, GPM Microwave Imager; IMERG, Integrated Multi satellitE Retrievals for GPM; PR, Precipitation Radar; TMI, TRMM Microwave Imager.
Source publication
Accurate, high‐resolution measurements of the precipitation diurnal cycle are important for understanding local variations in precipitation and the underlying processes which cause them. Combining 16 years of measurements from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and 4 years from the Global Precipitation Measureme...
Citations
... The IMC region, which has a complex topography and high solar insolation, allows for rain events due to very narrow local convection (Yamanaka, 2016). It was confirmed by Hayden and Liu (2021) that tested the validation of IMERG data with Ku-band radar observations. They found that IMERG data underestimates shallow (small-scale) and overestimates deep system (large-scale) rainfall over the tropical land region (20°S-N). ...
The availability of surface rainfall data with high spatial-temporal resolution is needed to understand the diurnal rainfall characteristics in the Indonesian Maritime Continent (IMC) to improve the accuracy of weather and climate models in this region. This study validates the accuracy of the Final Run product of IMERG data version 06B (V06B) and version 07A (V07A), which have a resolution of 0.1°–30 min for diurnal rainfall analysis over IMC. Validation was conducted for precipitation amount (PA), precipitation frequency (PF), and precipitation intensity (PI), by recording 302 automatic rain gauges (RG) every 10 min from January 2014 to September 2021. IMERG V06B and V07A perform well in observing diurnal PA and PF but struggle in observing diurnal PI compared to RG observations. This has been determined by the correlation coefficient (CC) values of IMERG V06B (V07) data, which are 0.76 (0.72) for PA, 0.77 (0.76) for PF, and 0.21 (0.13) for PI. The IMERG data align to an extent with RG observations for the PA value, having a small relative bias (RB). The results also display a systematic PF and PI values error. The high false alarm ratio (FAR) of IMERG data suggests rain detection errors, leading to overestimated PF values. Additionally, the underestimation of PI values is due to the limitation of IMERG data in observing extreme and small-scale rain events. About 86.10% (78.14%) and 81.45% (81.78) of IMERG V06B (IMERG V07A) data show a peak time difference of <3h for PA and PF when compared with RG observations. Overall, IMERG V06B performs better than V07, possibly due to inaccurate orbits of GPROF data, removal of SAPHIR satellite observations, and inter-calibration issues with CORRA and GPCP data. Continuous monitoring and data improvements are necessary to improve the accuracy and reliability of IMERG data in detecting diurnal rainfall patterns in the IMC region.
... The manifestation of such shallow clouds is discernible through elevated BT values, as illustrated in Fig. 2. In line with the findings of Stephens et al. (2019), a substantial proportion of tropical precipitation is associated with relatively shallow or low-altitude clouds. Additionally, the research by Hayden and Liu (2021) underscores an underestimation of shallow clouds generating morning precipitation due to a deficiency in ice formation. Notably, the Himawari-8 observation, as highlighted by Ahmad et al. (2020) faces limitations in detecting low-level cloud events. ...
The accurate prediction of extreme rain events is essential for the management and mitigation of hydrometeorological disasters. The Himawari-8 satellite provides cloud observations that can accurately forecast short-term extreme rain events using the Brightness Temperature (BT) data and Brightness Temperature Difference (BTD) method. The statistical evaluation of this method was conducted using optical rain gauge data taken from Kototabang, West Sumatra, Indonesia (100.32° E, 0.20° S) to determine the best threshold for detecting extreme rain. We used three bands, bands 11 (B11), 13 (B13) and 15 (B15) and tested three combinations of BTDs from these bands, namely BTD1 (B11-B13), BTD2 (B13-B15), and BTD3 (BTD1-BTD2). This study emphasizes the importance of parameter selection in extreme rain identification and forecasting using cloud BT and BTD methods. Effective parameter optimization is essential for adapting these approaches to different rainfall intensities, therefore selecting appropriate thresholds is necessary. The research particularly highlights the impact of temperature and rainfall intensity on BT accuracy, with BT excelling at 250 K for light rain but showing reduced accuracy as rainfall intensity increases. BTD1 demonstrates improved accuracy with higher rainfall intensity, especially at a 3 K threshold, allowing for predictions of extreme rain events with a 10–20 minute lead time. However, the limitation of this threshold is shown by consistent critical success index (CSI). Therefore, BTD1 with threshold 0 K give better performances with good accuracy, CSI and false alarm ratio (FAR) for various rain intensities. BTD2 shows improved accuracy at lower thresholds with reduced rainfall intensity and at the 3 K threshold, offering potential for extreme rainfall anticipation, but with declining CSI as rainfall intensity rises. The 0 K threshold, despite high probability of detection (POD), yields increased FAR in moderate to severe rainfall scenarios. BTD3 generally exhibits increased accuracy and CSI with rising rainfall intensity, except at the −3 K threshold, with the 0 K threshold standing out as the optimal choice, providing a 10–20 minute lead time for intense rain predictions. This study shows that the selection of the appropriate BT and BTD techniques, and parameter values should align with specific rainfall levels and forecasting goals.
... Then, 20 years of TRMM and GPM Ku-band radar near surface rain rate and their sample area, including the rain area and volume after taking into account sampling pixel sizes, are composited into local hourly precipitation properties for each month of the year, with a resolution of 0.1 • × 0.1 • . Though there are slight differences in the detection sensitivity before and after the boost of TRMM PR as well as the GPM KuPRF retrievals, this is not a big concern in the diurnal variation after the composite of 20 years of data [37]. And it is reasonable to study the spatial distribution of diurnal precipitation properties at 0.1 • resolution [45]. ...
... To quantify the diurnal characteristics of PA, PF, and PI, we conduct a Fourier harmonic analysis of the climatological diurnal PA, PF, and PI, which has been used in past studies [2,8,37]. Using the Fast Fourier Transform (FFT), we calculate the amplitude and (5) where F(u) is the Fourier component for the uth harmonic mode and R(h) is the hourly PA/PF/PI value at the local hour, h. ...
... To quantify the diurnal characteristics of PA, PF, and PI, we conduct a Fourier harmonic analysis of the climatological diurnal PA, PF, and PI, which has been used in past studies [2,8,37]. Using the Fast Fourier Transform (FFT), we calculate the amplitude and phase of hourly precipitation at each 0.1° × 0.1° grid point for the above 3 precipitation features. ...
Based on the 20-year high-resolution precipitation data from TRMM and GPM radar products, diurnal features over complex terrains along the Yangtze River (YR) are investigated. Using the Fast Fourier Transform (FFT) method, the first (diurnal) and second (semi-diurnal) harmonic amplitude and phase of precipitation amount (PA), precipitation frequency (PF), and intensity (PI) are analyzed. The diurnal amplitudes of PA and PF have a decreasing trend from the west to the east with the decreasing altitude of large-scale terrain, while the semi-diurnal amplitudes of PA and PI depict the bimodal precipitation cycle over highlands. For the eastward propagation of PA, PF is capable of depicting the propagation from the upper to the middle reaches of YR, while PI shows the eastward propagation from the middle to the lower reaches of YR during nighttime and presents sensitivity to highlands and lowlands. According to the contribution of different-sized precipitation systems to PI over the highlands and lowlands, the small (<200 km2) ones contribute the least while the large ones (>6000 km2) contribute the most, but the medium ones (200–6000 km2) show a slightly larger contribution over the highlands than over the lowlands. The propagation of each scaled precipitation system along the YR is further analyzed. We found that small precipitation systems mainly happen in the afternoon without obvious propagation. Medium ones peak 2–4 h later than the small ones, with two eastward propagation directions at night from the middle reaches of YR to the east. The large ones are mainly located in lowlands at night, with two propagation routes in the morning over the middle and lower reaches of YR. Such a relay of the propagation of the medium and large precipitation systems explains the eastward movement of PI along the YR, which merits future dynamic studies.
... Some evaluation studies indicate that IMERG tends to underestimate precipitation associated with tropical cyclone precipitation over the United States (e.g., Mazza & Chen, 2023;F. Tian et al., 2018), while a study by Hayden and Liu (2021) showed both regional under-and over-estimates in the tropics. In addition, many modeling studies performed at higher resolutions show a high bias in land convection over the MC; at lower resolutions, the timing of the DC as well as its amplitude are frequently misrepresented (e.g., Love et al., 2011;Watters et al., 2021). ...
Large‐scale convection associated with the Madden‐Julian Oscillation (MJO) initiates over the Indian Ocean and propagates eastward across the Maritime Continent (MC). Over the MC, MJO events are generally weakened due to complex interactions between the large‐scale MJO and the MC landmass. The MC barrier effect is responsible for the dissipation of 40%–50% of observed MJO events and is often exaggerated in weather and climate models. We examine how MJO propagation over the MC is affected by two aspects of the MC—its land‐sea contrast and its terrain. To isolate the effects of mountains and land‐sea contrast on MJO propagation, we conduct three high‐resolution coupled atmosphere‐ocean model experiments: (a) control simulation (CTRL) of the 2011 November–December MJO event, (b) flattened terrain without MC mountains (FLAT), and (c) no‐land simulation (WATER) in which the MC islands are replaced with 50 m deep ocean. CTRL captures the general properties of the diurnal cycle of precipitation and MJO propagation across the MC. The WATER simulation produces a more intense and smoother‐propagating MJO compared with that of CTRL. In contrast, the convergence of sea breezes in the FLAT simulation produces much more organized convection and precipitation far inland than in CTRL, which results in a stronger barrier effect to MJO propagation. The land‐sea contrast induced land‐locked convection weakens the MJO's convective organization. The land‐locked convective systems over land in FLAT are more intense, grow larger, and last longer, which is more detrimental to MJO propagation over the MC, than the mountains that are present in CTRL.
... This criterion distinguishes between the detected core and the anvil or cirrus, as done by Pan et al. (2021). The IMERG data has a high sensitivity to non-rain ice particles, which possibly overestimate the probability of weak rainfall (Cui et al., 2020;Hayden & Liu, 2021;Zhang et al., 2021). The combined criteria of BT and rainfall rate for convective core are beneficial to constrain the effect of observed data uncertainty on cloud identifications. ...
... However, due to the large number of short-lived DCSs, the integrated rainfall amount at each interval of DCS lifetime are comparable with each other (blue line in Figure 7a). Previous studies indicated that the IMERG data possibly overestimate the probability of weak rainfall, generating false rainfall in anvil regions (Cui et al., 2020;Hayden & Liu, 2021;Zhang et al., 2021). This may result in the overestimation of the rainfall amount of the tracked DCSs. Figure 5 shows that the rainfall amount is dominated by rainfall in core areas, while the rainfall in broad anvil areas contributes little to the total rainfall amount of DCSs. ...
Accurate tracking of all components (including core, anvil, and cirrus) of deep convective systems (DCSs) throughout their lifecycle is key to quantifying their impacts on radiative forcing, especially of the anvil and cirrus. Here, a new Full‐tracking Algorithm for Convective Thunderstorm System is developed based on geostationary satellite. It successfully tracks DCSs starting from the initial core to complete dissipation of cirrus detrained from them, and integrates all the related components that split from the initial convective core into a whole DCS. Results show that more than half of the tracked DCSs experience splitting evolutions, with an average of eight sub‐cores during their lifetime. With tracking cirrus generated by DCSs, the lifetime of DCSs is lengthened by up to 10 hr, and their area is enlarged by 16% on average. Generally, long‐lived DCSs have lower cloud top temperature, greater rainfall, and larger area, with more frequent splitting evolutions than short‐lived DCSs. Additionally, DCSs always reach their peaks within 6 hr after initiation regardless of their lifetime. This paper provides a basis for further quantifying the evolution of DCS properties, their impacts on the global radiation budget, and the water cycle in the climate system.
... Thus, hourly IMERG data can be used to observe diurnal patterns in the IKN area. Previous researchers have also demonstrated the ability of IMERG data to observe hourly rainfall through time series analysis (S. S. Yusnaini et al., 2021)and diurnal cycle analysis (Ahmed et al., 2021;Hayden and Liu, 2021;Li et al., 2018;Watters and Battaglia, 2019). ...
The Indonesian government has decided to move its capital city from Jakarta in Java to Nusantara Capital City (IKN) on Borneo island. This study investigates the trend and variability of rainfall in the IKN and two buffer cities and its relation to hydrometeorological disasters. We analyze 20 years of Integrated Multi-Satellite Retrievals for GPM (IMERG) version 6 data and hydrometeorological disaster information from the National Agency for Disaster Countermeasure. The annual rainfall, the extreme rain index of R95p (number of very wet days), and R99p (number of very extremely wet days) show a slightly decreasing trend. However, the index of consecutive wet days (CWD) increases, resembling the rising number of floods and landslides. Rainfall shows robust seasonal and diurnal variations. Peak rainfall occurs in November–December and March–April, while the driest period is observed during August–October. Dry months associated with El-Nino, can cause severe dry conditions and increase the potential for catastrophic forest fires. The peak precipitation amount and frequency were observed in the early morning and the second peak in the afternoon. Mainland areas tend to have a peak occurring later than those on the coast and ocean. The results of this study can be additional information in formulating a strategic plan to anticipate future hydrometeorological disasters in IKN.
... As previously stated, the WWLLN data set is limited by the detection capabilities of the network, especially in remote regions, and the tracking algorithm is limited by its inability to distinguish two similarly sized storms close together as different systems. Additionally, the IMERG data set is limited to studies using full days of data only, as the differing detection capabilities of the satellites used throughout the day lead to inconsistencies in diurnal variations measured using this data (Hayden & Liu, 2021;Tan et al., 2018). Since PMW measurements are not available constantly, IMERG uses PMW precipitation measurements when they are available and relies more heavily on geostationary IR measurements during the other hours of the day. ...
Mesoscale convective systems (MCSs) occur frequently over the tropics and mid‐latitudes and have a large impact on the local precipitation amounts as well as large‐scale circulation through their modulation of the vertical diabatic heating profile. To fully understand and quantify these effects, MCSs must be studied throughout their lifetimes at both mid‐ and tropical latitudes, over both land and ocean. This can be accomplished by tracking the storm using a global scale data set of precipitation and using this information to composite collocated active sensor measurements to produce a detailed analysis of storm properties along the lifetime of the MCS. To do this, we utilize precipitation features (PFs) produced using observations from the Global Precipitation Measurement (GPM) mission's core satellite and from Integrated Multi‐satellitE Retrievals for GPM data by grouping contiguous raining pixels in both data sets. We propose a simplified tracking algorithm to track systems throughout their lifetimes. Lightning data from the World Wide Lightning Location Network are collocated to these tracks along with GPM PFs. These are then composited relative to the time step along the track that has the greatest number of lightning flashes, which is used as a proxy for MCSs with lightning reaching the maximum convective intensity. We then examine various radar variables for tropical and mid‐latitude systems of varying lifetimes over both land and ocean in order to determine the differences and similarities between these types of systems.
Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using spaceborne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest (RF) model to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands—a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF model is trained on five years of data (2016–20) and tested with two independent years (2015 and 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA5 2-m temperature and freezing level reanalysis and/or Dual-Frequency Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb values as nonshallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb values, likely resulting from the presence of ice particles in nonprecipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.
Significance Statement
Published research concerning rainfall retrieval algorithms from microwave radiometers is often focused on the accuracy of these algorithms. While shallow precipitation over land is often characterized as problematic in these studies, little progress has been made with these systems. In particular, precipitation formed by shallow clouds, where shallow refers to the clouds being close to Earth’s surface, is often missed. This study is focused on detecting shallow precipitation and its physical characteristics to further improve its detection from spaceborne sensors. As such, it contributes to understanding which shallow precipitation scenes are challenging to detect from microwave radiometers, suggesting possible ways for algorithm improvement.
The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017–2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation.
NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory ’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers.
Significance Statement
Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.
