Variations in the Diurnal Cycle of Precipitation and Its Changes with Distance from Shore over Two Contrasting Regions as Observed by IMERG, ERA5, and Spaceborne Ku Radar

Lindsey J. M. Hayden aJoint Center for Satellite Data Assimilation, University Corporation for Atmospheric Research, Boulder, Colorado

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Jackson Tan bUniversities Space Research Association, Greenbelt, Maryland
dNASA Goddard Space Flight Center, Greenbelt, Maryland

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David T. Bolvin cScience Systems and Applications Inc., Greenbelt, Maryland
dNASA Goddard Space Flight Center, Greenbelt, Maryland

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George J. Huffman dNASA Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing, highly localized effects. The diurnal cycle in regions such as the Amazon and the Maritime Continent are of particular interest, due to the complex coastal and terrain effects. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset are used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-yr (2000–18) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on half-hourly averaged precipitation at 0.1° × 0.1°. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broadscale patterns of diurnal variability but does not capture all the fine-scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long-record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lindsey J. M. Hayden, haydenlj@ucar.edu

Abstract

The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing, highly localized effects. The diurnal cycle in regions such as the Amazon and the Maritime Continent are of particular interest, due to the complex coastal and terrain effects. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset are used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-yr (2000–18) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on half-hourly averaged precipitation at 0.1° × 0.1°. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broadscale patterns of diurnal variability but does not capture all the fine-scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long-record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lindsey J. M. Hayden, haydenlj@ucar.edu

1. Introduction

Variation in precipitation occurs globally on a continuum of time scales, from interannual variations driven by cycles such as El Niño–Southern Oscillation (ENSO), to seasonal and subseasonal, driven by changes in the incoming solar radiation and smaller-scale oscillations such as the Madden–Julian oscillation (MJO), respectively. Daily and subdaily variations in precipitation are the most prominent peaks on the shorter end of the continuum. The diurnal cycle of precipitation is highly localized and regionally variable. While the cycle is driven in the large scale by the diurnal cycle of incoming solar radiation, small-scale processes such as terrain and land–sea breeze circulation drive the local hourly precipitation. Satellite-based precipitation observations are useful for monitoring precipitation, especially since they can observe regions where ground-based measurements are sparse. Datasets such as the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG; Huffman et al. 2020) utilize observations from multiple satellite platforms in order to produce precipitation estimates at higher spatial and temporal resolution. The abundance of samples makes the IMERG observations well suited to high-resolution studies of the diurnal cycle (Tan et al. 2019b). Ku radar observations by the Tropical Rainfall Measuring Mission (TRMM; Kummerow et al. 1998) Precipitation Radar (PR) and Global Precipitation Measurement (GPM; Skofronick-Jackson et al. 2017) mission Core Observatory (CO) Dual-frequency Precipitation Radar (DPR) can also be used to produce a high-resolution spaceborne precipitation dataset. These active measurements have lower sampling capability than IMERG but are not subject to the types of errors affecting the precipitation timing and intensity of the passive measurements used in IMERG described in O and Kirstetter (2018).

Reanalysis models, such as ERA5 (Hersbach et al. 2020), are also used for regional studies of the precipitation diurnal cycle (e.g., Chen et al. 2014; Qin et al. 2021; Jiang et al. 2021). These models have the advantage of consistent global coverage as well as providing variables beyond precipitation, such as temperature, humidity, and water content at multiple vertical levels (Vousdoukas et al. 2016). However, most reanalysis datasets do not assimilate observed precipitation, but rather compute it by numerical models, which rely on parameterization schemes that may suffer from inaccuracies in their representations of the underlying precipitation processes. The assimilation of ground-based radar precipitation by ERA5 is a recent upgrade, as the previous version (ERA-Interim) did not assimilate these data (Hersbach et al. 2020), and is only available over select regions. Several studies have been conducted to evaluate the accuracy of ERA5 precipitation at various spatial and temporal scales (e.g., Nogueira 2020; Beck et al. 2019; Xu et al. 2019; Amjad et al. 2020; Qin et al. 2021). Beck et al. (2019) found that ERA5 performed better than IMERG over areas of complex terrain but worse over areas dominated by short-lived convective systems at the daily time scale over the United States. On the other hand, Sharifi et al. (2019) found that IMERG outperforms ERA5 at the daily and monthly time scales over the mountainous terrain in Austria. Performing the comparison in the subdaily time scales, Tang et al. (2020), Gao et al. (2020), Kumar et al. (2021), and Hong et al. (2021) all found that ERA5 struggles with the diurnal cycle compared to IMERG. It is therefore important to compare satellite- and reanalysis-based precipitation datasets in multiple locations, as well as at subdaily temporal scales in order to understand the strengths and weaknesses of each.

Fast Fourier transform (FFT) analysis provides an objective analysis method for diurnal and subdiurnal variation. This technique has been demonstrated for analysis of high-resolution satellite precipitation observations on a global scale by Hayden and Liu (2021). This type of analysis is useful on a regional scale, as it reveals small-scale features. We also examine the changes in these properties with distance from shore, which helps to reveal large-scale propagation patterns as well as the processes at work behind this propagation.

Regions with high spatial variability in the precipitation diurnal cycle pose a challenge for satellite and reanalysis datasets alike. It is important to determine differences between these datasets in such areas in order to test the performance of each precipitation dataset in these areas. Precipitation in the Maritime Continent has long been studied due to its importance to global circulation (e.g., Zhou and Wang 2006). The diurnal cycle in this region is highly variable, due to the complex coastline and interaction of convection with the topography of the region (Holland and Keenan 1980; Zhou and Wang 2006; Tan et al. 2022). Assessing the relative contribution of the diurnal and semidiurnal components of variability to the average daily precipitation may assist in studying this complexity by highlighting regions where differing forcings are not temporally synchronized, resulting in multiple daily peaks in precipitation. The impact of the coastline on the precipitation has been identified as a key component of the variability in this region, thus it is useful to study the changes in precipitation relative to the coastline in detail. The complexities and variability of precipitation in the Maritime Continent make it an important region to verify that the correct diurnal cycle of precipitation is being measured and simulated, and a good place to test the limitations of each precipitation dataset.

Amazonia is also an important region to verify the correct determination of the precipitation diurnal cycle, due to the variability of precipitation being a major source of uncertainty in catchment hydrology (e.g., Ruiz-Hernandez et al. 2021) and the Amazon basin being the largest drainage basin in the world (Zubieta et al. 2019). Propagating convective systems have been described as a key component of the precipitation in this region (e.g., Tai et al. 2021) while also having been described as a deficiency in model and reanalysis simulations (Betts and Jakob 2002; Itterly et al. 2018, 2016; Itterly and Taylor 2014). It is therefore important to describe the timing and diurnal cycle of these systems as they propagate inland from coastal regions. This region also provides a contrast to the Maritime Continent region, having less complex topography and coastlines, as well as being largely land based. Ground-based observations in these regions are available (e.g., Ruiz-Hernandez et al. 2021; Lu et al. 2019), but they are limited to specific, small-scale regions such as cities and catchment basins that are the subject of field studies. Using satellite precipitation allows the entire Amazon to be viewed with consistent resolution, allowing large-scale patterns to be seen, as well as small-scale features present in remote, ungauged regions. We therefore choose to look at the diurnal cycle of these regions in detail and compare the precipitation from the three datasets under a variety of local influences.

2. Data and methods

a. IMERG

IMERG makes use of passive microwave (PMW) retrievals from the GPM constellation satellites and infrared observations from geostationary satellites when needed in order to produce a half-hourly, 0.1° × 0.1° global precipitation dataset (Huffman et al. 2020). It is available in three runs, Early, Late, and Final, with progressively increasing latency and accuracy. PMW precipitation, as estimates by the Goddard profiling algorithm (GPROF; Kummerow et al. 2015) and Precipitation Retrieval and Profiling Scheme (PRPS; Kidd et al. 2021) applied to specific satellite PMW records are gridded to 0.1° × 0.1° grid boxes every half-hour and intercalibrated to instruments on board the GPM CO. This intercalibration process involves adjusting the mean precipitation estimates from each of the constellation sensors to the GPM Microwave Imager (GMI) estimates and then applying calibration factors calculated from collocated GMI and the GPM Combined Radar and Radiometer Algorithm (CORRA; Grecu et al. 2016; Tan et al. 2019a) retrievals to all PMW estimates. Precipitation estimates from the TRMM satellite are similarly used to calibrate PMW data, allowing IMERG to be computed during the TRMM era. CORRA estimates are adjusted to the monthly climatology of the Global Precipitation Climatology Project version 2.3 (Adler et al. 2003, 2016) observations at mid- and high latitudes to account for known deficiencies (Tan et al. 2019a). After calibration, gaps in the PMW precipitation field are filled using a combination of quasi-Lagrangian interpolation and microwave-calibrated geosynchronous-Earth-orbit infrared precipitation estimates. Additionally, in the IMERG Final Run, the precipitation estimates are calibrated to a combination of monthly IMERG satellite-only estimates and Global Precipitation Climatology Center (GPCC; Schneider et al. 2013) monthly gauge analysis. See Huffman et al. (2020) and Tan et al. (2019a) for a more complete description of the algorithm. Here, we use 18 years (2001–18) of IMERG version 06B Final run for comparison with the TRMM–GPM radar and PMW data, averaging the half-hourly data to hourly resolution. The data are given in coordinated universal time (UTC); thus, to derive diurnal variation of precipitation, a longitude-based conversion is applied to produce precipitation in 24-hourly local solar time (LST) bins.

b. Ku radar

The 20-yr Ku radar dataset is comprised of precipitation observations from 16 years (1998–2013) TRMM PR combined with 4 years (2015–18) of GPM DPR data. Both instruments observe precipitation at the Ku-band frequency, and the DPR was built as the successor to the PR. A similar dataset was used to study the diurnal cycle of precipitation and compare the observations from multiple satellite datasets globally at high resolution by Hayden and Liu (2021).

The TRMM satellite (Kummerow et al. 1998) was launched on 27 November 1997 and collected data until 8 April 2015. It had an orbital inclination of 35° and orbited at an altitude of 350 km until 6 August 2001, when it was boosted to an altitude of 403 km. The PR was a Ku-band (13.8 GHz; 21.7 mm) scanning radar with a horizontal resolution of approximately 5 km and a vertical resolution of approximately 250 m at nadir. Version 7 PR precipitation retrievals (Iguchi et al. 2009) have been used to form the 16-yr TRMM dataset. August 2001 and June 2009 have some missing data due to the orbital boost and an instrument problem, respectively. To deal with this missing data, we have replaced these months with the corresponding full months of data from 2014. This method follows that of Hayden and Liu (2021) and does not noticeably affect the climatological analysis of the diurnal cycle. The PR near-surface precipitation rate data are gridded into 0.1° × 0.1°, hourly grid boxes (in LST) over the TRMM region (35°N–35°S) for combination with the GPM DPR observations.

The GPM CO was launched on 27 February 2014. It orbits at an altitude of 407 km with an inclination of 65° (Hou et al. 2014; Skofronick-Jackson et al. 2017). The DPR operates at both Ka-band (KaPR; 35.5 GHz; 8.5 mm) and Ku-band (KuPR; 13.6 GHz; 22 mm) radar frequencies, with the Ku having a horizontal resolution of 5 km and a vertical resolution of 250 m at nadir. Four years of version 6 KuPR precipitation data are gridded into the same hourly 0.1° × 0.1° grid boxes as the PR, then the Ku radar datasets are combined. Following the method of Hayden and Liu (2021), 20-yr volumetric precipitation retrievals and pixel sample areas for the TRMM and GPM Ku radars are accumulated at these grid boxes. Then the mean precipitation rate at each hourly 0.1° × 0.1° grid box is computed by dividing the total precipitation volume by total sample area in each grid box.

Both TRMM and GPM are in non-sun-synchronous orbits, which allows the full diurnal cycle to be sampled. The PR and DPR swaths are narrow (215 and 245 km, respectively), however, and each overpass at a given location occurs only once every 2–3 days, or less often (Nesbitt and Zipser 2003). For the TRMM altitude/inclination, sampling a full diurnal cycle in the tropics takes approximately 46 days (Hirose and Nakamura 2005), while it is about 83 days for the GPM CO’s altitude/inclination. Thus, creating a precipitation climatology from TRMM and/or GPM data requires as many years as possible in order to create robust statistics. In the past, there have been attempts to study diurnal variations of precipitation at high resolution using spaceborne precipitation radars (Hirose et al. 2008; Nesbitt and Anders 2009; Biasutti et al. 2012), but the limited samples have always been an issue. Here, 20 full years of Ku radar data are used as an attempt to mitigate the sampling concerns.

Note that during the TRMM era, the precipitation retrievals are not homogenous in the long-term due to the change in the spatial resolution (4.3–5 km) and sensitivity of PR (reduced by 1.21 dBZ) after the orbit boost in 2001 (Shimizu et al. 2009). GPM KuPR also has a better sensitivity than TRMM PR (minimum detectable signals of 12 and 18 dBZ, respectively) so that it detects more weak precipitation (Hamada and Takayabu 2016). These instrumental changes do not affect this study since the long-term variation of precipitation is not the focus here. Diurnal variation of precipitation should be reasonably robust from 20-yr averages at high temporal and spatial resolutions. However, it should be remembered that Ku-band radars on board TRMM and GPM still miss light precipitation rates (Lebsock and L’Ecuyer 2011; Hayden and Liu 2018). Therefore, in the following analysis, light precipitation (e.g., <0.1 mm h−1) is considered to be zero in all datasets.

c. ERA5

The ECMWF’s ERA5 global reanalysis (Hersbach et al. 2020) is based on the ECMWF Integrated Forecasting System (IFS) Cycle 41r2 and uses 4D-Var data assimilation to combine observations with model simulations in order to provide a consistent record of atmospheric conditions globally at 31 km, hourly resolution. ERA5 data are available beginning in 1979. Observational data are assimilated in 12-h windows and includes assimilation of land data, daily sea surface temperature, and sea ice concentration. Six-hourly precipitation estimates over the contiguous United States from the National Centers for Environmental Prediction (NCEP) Stage IV radar–gauge product are assimilated (Lopez 2011; Hersbach et al. 2020), as well as brightness temperatures from GPM/TRMM PMW constellation members, and satellite derived atmospheric motion vectors, in addition to conventional measurements such as aircraft, radiosonde, dropsonde, and GPS radio occultation profiles (Hersbach et al. 2020). The model includes coupling between the atmosphere and the land/ocean and handles convective parameterization using a bulk mass flux scheme (ECMWF 2020).

d. Methods

At each 0.1°, we compile the diurnal variability by averaging the unconditional (i.e., including zeros) precipitation rates at each time step for the entire record. To conduct a comparison of the precipitation diurnal cycle from the three datasets, we conduct a Fourier harmonic analysis. This method has been used in past studies (Nesbitt and Zipser 2003; Liu and Zipser 2008, Hayden and Liu 2021). Using an FFT, we calculate the phase and amplitude of the hourly precipitation in the native resolution of each of the three datasets. The discrete FFT is calculated as
F(u)=124h=023R(h)eiπuh/24,
where F(u) is the Fourier component for uth harmonic mode and R is the hourly precipitation rate at the local hour h. Thus, the amplitude of the uth harmonic mode (Au) is given as
Au=2×|F(u)|
and indicates the percentage deviation of the uth harmonic mode from the mean.
To identify the dominance of a harmonic mode, we consider the magnitude of different harmonic modes relative to the total amplitude. This allows us to identify the contributions of the various harmonic modes to the diurnal variability, which can reveal, for example, the dominance of the first harmonic (diurnal variability) over the second harmonic (semidiurnal variability) and higher harmonics (which is interpreted here as noise). The relative magnitude of the first harmonic amplitude is defined as
H1=A1i=1uAi.
The relative magnitudes of the second harmonic amplitude (H2) and the relative magnitudes of the higher harmonic amplitudes (H3+) are defined similarly.
The phase of the uth harmonic mode (φu) is given as
φu=24tan1[F(u)]%24,
where %24 indicates a modulo operation with respect to 24.

3. Results

a. Maritime Continent

The Maritime Continent is prone to large variations in the local diurnal cycle of precipitation due to the presence of multiple mountain ranges and coastlines. The average daily precipitation for each of the three datasets is shown in Fig. 1 in the native resolution for each dataset. Higher precipitation can be seen over the terrain in each of the three datasets, especially over New Guinea and Borneo, as well as over the ocean west of Sumatra and north of New Guinea (Fig. 1). The Ku radar dataset (Fig. 1a) is relatively noisy due to the lower number of samples, however, several small-scale features can be seen in both observational datasets, such as the decrease in precipitation over the Sulu Islands in the southwest of the Philippines (∼6°N, 120°W) and the increase in precipitation over the Straits of Malacca between Sumatra and the Malaysian peninsula (∼3°N, 100°W; Figs. 1a,b). These features can also be seen in the ERA5 reanalysis (Fig. 1c), though the variability is lower. In general, IMERG produces higher precipitation rates, particularly over ocean, than the Ku radar dataset, while ERA5 has lower precipitation rates over land and reduced spatial variability over ocean.

Fig. 1.
Fig. 1.

Daily mean precipitation observed by (a) Ku radar and (b) IMERG at 0.1° × 0.1° resolution, and (c) ERA5 at 0.25° × 0.25° resolution.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

The hourly domain-average precipitation from each of the three datasets is shown in Fig. 2. Ku radar and IMERG precipitation have been averaged to 0.5° × 0.5° hourly to match the ERA5 resolution in this figure, for a more direct comparison. All three precipitation datasets show the expected late afternoon precipitation peak over land (red) and lower amplitude morning peak over ocean (blue). ERA5 shows the lowest amplitude diurnal cycle over land, overestimating the precipitation in the morning minimum in comparison to the satellite observations. ERA5 also produces an afternoon peak of precipitation 1–2 h earlier than observed by either Ku radar or IMERG. IMERG precipitation is higher than that observed by Ku radar at all times of day, over both land and ocean. IMERG V06 is known to have bias issues and the overestimation by IMERG compared to Ku radar was also observed over the entire tropics (20°N–20°S) by Hayden and Liu (2021).

Fig. 2.
Fig. 2.

Diurnal cycle of precipitation over the Maritime Continent averaged to ERA5 resolution.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

Diurnal phase (time of the maximum of the first harmonic in LST) for each of the three datasets is shown in Fig. 3. In the Ku radar observations (Fig. 3a), a change of phase as a function of the distance to the coastline, likely due to land–sea breeze circulations, can be seen both over land and over ocean. Over ocean, the precipitation peak occurs in the early morning near the coastlines and moves to progressively later times of day with increasing distance from the coast (Fig. 3). This pattern continues until such a distance where an early afternoon peak is reached. The precipitation then returns to the expected open ocean pattern (e.g., Albright et al. 1981; Dai et al. 2007; Gray and Jacobson 1977; Liu and Zipser 2008; Nesbitt and Zipser 2003, Reed and Jaffe 1981, among others), noisy, due to the low amplitude, but with a general early morning peak. A similar pattern of phase shifting to later times of day with increasing distance from shore was seen by Bai and Schumacher (2022, their Fig. 6), including the timing of the peaks. Over land, the precipitation peaks around midafternoon near the coastline and progressively later in time further inland. This pattern can easily be seen in IMERG observations (Fig. 3b) and can also be seen in Ku radar observations (Fig. 3a), despite the noise. ERA5 (Fig. 3c) picks up this pattern, but the timing of the peak precipitation is slightly early compared to the two satellite datasets, over land as well as the near-coastal ocean. The terrain can also be seen to influence the diurnal phase of precipitation in this region (Fig. 3). This is especially evident over New Guinea, which has an east–west mountain range through the middle of the island; for example, moving northward along the 140°E meridian, where the phase can be seen to transition sharply from evening to late afternoon when approaching the terrain and transitions back to evening on the northern slope. This can be seen in both Ku radar (Fig. 3a) and IMERG (Fig. 3b). A similar phase structure was observed using TRMM by Zhou and Wang (2006). This can also be seen in ERA5 (Fig. 3c), though it is a less prominent transition and occurs at earlier times of day.

Fig. 3.
Fig. 3.

Diurnal (first) harmonic phase of hourly precipitation observed by (a) Ku radar and (b) IMERG at 0.1° × 0.1° resolution, and (c) ERA5 at 0.25° × 0.25° resolution.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

Figure 4 shows the relative amplitude of the diurnal cycle (first harmonic; H1), semidiurnal cycle (second harmonic; H2), and noise (third and higher harmonics; H3+) calculated from IMERG precipitation observations in Figs. 4a–c, respectively. The relative amplitude in these figures is calculated as defined in Eq. (3). High H1 values (Fig. 4a) are seen over the islands, especially near terrain, and over ocean near the coasts. These areas have a strong diurnal precipitation peak which is likely governed by terrain effects and land–sea breezes, respectively. This indicates that the Maritime Continent (except for open ocean) is dominated by the diurnal cycle, which is responsible for more than half of its subdaily variability.

Fig. 4.
Fig. 4.

Normalized FFT amplitude calculated from IMERG precipitation for the (a) first harmonic and (b) second harmonic. (c) High (third or higher) harmonic amplitudes are interpreted as noise.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

H2 values are generally low over the entire region (Fig. 4b), indicating the minimal contribution of semidiurnal processes throughout the region. There are several localized areas of higher H2 values, however, typically over land very near the coastline, for example, the northeast coast of Sumatra (∼4°S–2°N, ∼101°–105°W) and the north coast of New Guinea (∼3°S, ∼140°–145°W). Over New Guinea, the area experiences a morning peak in precipitation associated with the morning sea breeze, as well as a late afternoon peak associated with terrain-induced precipitation propagating toward the coast (not shown). Wei et al. (2020) showed a similar pattern of sea breeze/valley breeze interaction over the northeastern plain of Sumatra. This appears to be the case over most such regions, highlighting the complexity of the precipitation diurnal cycle in this region, due to the interaction of the terrain-induced circulation with the land–sea breeze. Values of H3+ (Fig. 4c) tend to be low over the islands and approximately complement H1 values over ocean. Over land and the near coastal ocean, the strong forcings allow for a robust diurnal signal in precipitation with minimal noise, as previously discussed. Over the open ocean, such forcings are weaker, especially relative to the artificial variability due to intersensor differences between the sun-synchronous PMW instruments (Tan et al. 2019b), the use of morphing, and the use of geosynchronous infrared estimates, resulting in a noisy signal.

From the results so far, the diurnal phase and amplitude exhibit a strong dependence on the distance from shore. Here, we further analyze these relationships for each of the three precipitation datasets. Figure 5 shows a two-dimensional histogram of the phase and amplitude as a function of distance from shore for all ocean pixels over the entire domain. Distance from shore is calculated as the Haversine distance to the coordinates of the nearest land pixel as defined on a 0.5° × 0.5° grid. Consistent with previous results (Figs. 3a,b), a shift toward later phase with distance from shore can be seen for both IMERG (Fig. 5a) and the Ku radar dataset (Fig. 5b). Yokoi et al. (2017) observed a similar offshore propagation of precipitation during a field campaign over Sumatra Island. ERA5 (Fig. 5c) captures a slight shift with distance from shore, however, the shift is not as distinct as that observed by the two satellite datasets, and the diurnal phase of ERA5 precipitation calculated at the majority of ocean pixels remains relatively constant, regardless of the distance from the shoreline. A decrease in amplitude with distance from shore is also observed by IMERG and the Ku radar dataset (Figs. 5d,e), implying a shift from a continental-like, surface-driven, strong diurnal cycle of precipitation to the expected weak open ocean pattern. As shown in Fig. 5f, ERA5 also shows a decrease in amplitude with distance from shore, but ERA5 does not exhibit the high amplitudes seen in Ku radar and IMERG near the coastline. At distances further than ∼200 km from shore, the amplitude remains relatively constant in all three datasets.

Fig. 5.
Fig. 5.

(a) IMERG, (b) Ku, and (c) ERA5 phase change with distance from shore over ocean. (d) IMERG, (e) Ku, and (f) ERA5 amplitude change with distance from shore.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

Figure 6 shows the phase and amplitude with distance from shore over land regions. Phase shifts to later times of day with increasing distance from the coast in all three datasets (Figs. 6a–c), similar to the pattern seen for ocean grid boxes but during the late afternoon/evening. Together with Fig. 5, this implies that the land–sea contrast is a driving factor of the precipitation diurnal cycle in most areas of this region, causing precipitation to form near the coastline and propagate out to sea in the early morning and inland in the afternoon/evening. This process is not well captured by ERA5 over land, though some propagation can be seen over ocean. The previously described phase advance can also be seen in these figures, especially over land (Fig. 6c). In contrast to ocean regions, a slight increase in amplitude with distance from shore is observed over land by IMERG and the Ku radar dataset (Figs. 6d,e), to a lesser extent.

Fig. 6.
Fig. 6.

As in Fig. 5, but for land regions.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

b. Amazonia

We also examine the diurnal cycle of Amazonia in detail. The mean precipitation over the region from the three datasets is shown in Fig. 7, similar to that shown in Fig. 1. Both satellite datasets show a similar pattern of precipitation over the region, with generally lower precipitation over the eastern and central portions of the continent, with increased precipitation in the northwestern portion of the landmass, especially in the region ∼4°S–4°N, 75°–65°W (Figs. 7a,b). ERA5 has a smaller range of precipitation over land in this region than either of the satellites (Fig. 7c). The precipitation maximum in this dataset is also closer to the central region of Amazonia (8°S–6°N, 70°–55°W) when compared to Ku radar and IMERG. Ku radar picks up some localized areas of increased precipitation near the eastern slopes of the Andes Mountains (Fig. 7a) which follow the terrain closely. IMERG (Fig. 7b) shows a few of these areas of localized increase in precipitation (e.g., 10°–8°S, 76°W; 2°S–1°N, 77°–76°W), however the enhancement is not as large as for the Ku radar. There are a few locations of slightly enhanced precipitation visible in ERA5 along the Andes (Fig. 7c), though they appear somewhat sporadic. An increase in precipitation can also be seen along the coast near the mouth of the Amazon River in all three datasets, albeit with slightly different magnitudes.

Fig. 7.
Fig. 7.

As in Fig. 1, but for the Amazon region.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

Figure 8 shows the average diurnal cycle over Amazonia averaged to ERA5 resolution similar to Fig. 2. Since the majority of this region is land, we restrict the calculation of the average diurnal cycle to land only. As seen over the Maritime Continent (Fig. 2), ERA5 appears to slightly overestimate the amount of precipitation over the entire region, especially when compared to the precipitation observed by the Ku radar dataset. The shift of ERA5 maximum diurnal precipitation to earlier in the day when compared to the Ku radar that was previously described in section 3a over the Maritime Continent land is even more apparent in this region. The lower range of precipitation rates from ERA5 seen in Fig. 7 is also visible in Fig. 8, implying a lower-amplitude diurnal cycle from ERA5 over this region as well. The IMERG daily precipitation peak is slightly delayed over this region compared to Ku radar. Similar phase delays in IMERG have been attributed to the effects of lingering anvils leftover from deep convection, which are interpreted as precipitation by the ice scattering signal from high-frequency passive microwave channels that dominate the IMERG precipitation estimates over land (Hayden and Liu 2021).

Fig. 8.
Fig. 8.

As in Fig. 2, but for the Amazon region, over land only.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

Diurnal phase over the region from the three datasets is shown in Fig. 9. As in previous figures, the Ku radar dataset (Fig. 9a) is noisy, but the pattern is similar to that observed by IMERG (Fig. 9b). Precipitation peaks around noon LST near the Atlantic coastline in both datasets. This diurnal phase shifts to gradually later times of day as the precipitation propagates inland. The propagation of phase continues overnight and into the next day, reaching a late afternoon peak in the western portion of the region. This is consistent with the results of previous studies such as Tai et al. (2021) and Dupuis and Schumacher (2018). This well-defined shift in peak precipitation from noon near the coast to near midnight ∼500 km from shore is likely associated with sea breeze induced disturbances forming near the coast and propagating inland, further triggering MCSs under convectively favorable environmental conditions (Tai et al. 2021). A similar propagation of phase can be seen moving eastward from the slopes of the Andes in both the Ku radar data and IMERG (Figs. 9a,b).

Fig. 9.
Fig. 9.

As in Fig. 3, but for the Amazon region.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

The phase lead by ERA5 is easily visible across the entire domain (Fig. 9c). ERA5 also shows relatively little spatial variation in diurnal phase when compared to the other two datasets. The widespread noontime peak in ERA5 precipitation implies that the precipitation over Amazonia in ERA5 is mainly driven by the solar cycle rather than small-scale propagation processes such as cold pools, which occur at scales smaller than model resolution. Reanalysis models have previously been shown to have difficulty simulating small-scale, short-lived convective systems (Adler et al. 2001; Arakawa 2004; Ebert et al. 2007; Beck et al. 2019).

Similar to Fig. 4, the relative contribution of each of the harmonic components to the total amplitude observed by IMERG is shown in Fig. 10 for Amazonia. Most land areas in the region are dominated by the diurnal harmonic amplitude (Fig. 10a). Some large areas of relatively higher-amplitude noise are seen near the center of the landmass (Fig. 10c), for example, from ∼4°S to 0° between 70° and 65°W. This region is not strongly or consistently influenced by the precipitation propagating inland from the Atlantic coast or the terrain induced precipitation propagating eastward from the Andes, which may lead to a weaker diurnal cycle. This, along with the high mean precipitation in this region (Fig. 7) implies that airmass thunderstorms dominate the precipitation in this region. Figure 10b shows several regions with relatively high semidiurnal harmonic amplitude, such as ∼4°–2°S, ∼56°–54°W. This area is located along the southern coast of the Amazon River, allowing the precipitation to be influenced by the passing line of precipitation propagating inland from the coast in the early morning and the Amazon River breeze circulation in the afternoon.

Fig. 10.
Fig. 10.

As in Fig. 4, but over the Amazon region.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

The change in phase and diurnal amplitude over land in this region is shown in Fig. 11. Unlike in the Maritime Continent (Figs. 5 and 6) where distances are from the nearest shore, the distances in this figure are calculated from the nearest Atlantic Ocean pixel because of the apparent asymmetry in diurnal variability between the Atlantic and Pacific sides of the continent (Fig. 9). The 2-day propagation with distance from the Atlantic coast seen in Figs. 9a and 9b, wrapping across the day boundary and appearing as two lines slanting lower left to upper right in both the IMERG (Fig. 11a) and the Ku radar (Fig. 11b) datasets. The phase changes with distance from shore at a fairly steady rate in both datasets until ∼750 km from shore. This is similar to results shown by Tai et al. (2021) and linked to the inland propagating disturbances caused by the sea breeze triggering MCSs which are sustained overnight by cold pool dynamics. These disturbances may then be reinvigorated by solar heating on the second day (Tai et al. 2021). IMERG observed MCSs lasting longer than 12 h were shown to take an east to west path in this region by Hayden et al. (2021, their Fig. 3). ERA5 shows very little change in phase with distance from shore in this region (Fig. 11c). IMERG (Fig. 11d) and Ku radar (Fig. 11e) also show similar changes in amplitude with distance from shore over this region. The highest amplitudes are found near the coast and decrease steadily until ∼500 km from shore, remaining approximately constant, or even slightly increasing after that. Tai et al. (2021) found that this is the approximate extent of direct influence of the sea breeze circulation in this region. ERA5 (Fig. 11f) shows a similar pattern with distance from shore but at highly muted amplitudes. The diurnal amplitude seen in ERA5 is typically less than half that seen in IMERG or Ku radar data at any given distance.

Fig. 11.
Fig. 11.

As in Fig. 6, but for the Amazon region. Distances are calculated as distance from the Atlantic Ocean.

Citation: Journal of Hydrometeorology 24, 4; 10.1175/JHM-D-22-0154.1

4. Conclusions

The diurnal cycle of precipitation has been examined over the Maritime Continent and Amazonia using ERA5, IMERG, and spaceborne Ku radar data. A Fourier transform was used to determine the diurnal phase and amplitude, as well as the relative contributions of the semidiurnal cycle and the noise component to the overall amplitude of daily precipitation. Over the Maritime Continent, all three datasets produce the expected low-amplitude early morning precipitation peak over the ocean and a higher amplitude, late afternoon peak over land. ERA5 produces a lower-amplitude diurnal cycle than either IMERG or Ku radar, over both land and ocean, with a slight phase lead over land. IMERG shows an overestimation of precipitation over the Maritime Continent compared to Ku radar, which has been previously described by Hayden and Liu (2021).

Amplitude and phase change with distance from shore have also been shown. Over the ocean, diurnal phase is near midnight LST close to the coast and shifts later in the day with increasing distance from shore, leveling off at the open ocean phase of 0500–0700 LST by ∼200 km from shore. This pattern is observed in both IMERG and the Ku radar dataset, with a similar pattern occurring in ERA5 data but with the phase leveling off at 0400–0600 LST at ∼100 km from shore. This pattern was also observed in previous studies such as Tai et al. (2021) and Dupuis and Schumacher (2018) and was linked to MCSs associated with the inland propagation of sea breeze disturbances around local noon which continue their propagation overnight, sustained by cold pool dynamics. Amplitude decreases with distance from shore in all three datasets, leveling off at ∼200–300 km from shore, the approximate distance Tai et al. (2021) described as the maximum range of direct influence of the sea breeze. IMERG and Ku radar both show a relatively large and rapid decrease in amplitude, while the decrease observed by ERA5 begins at lower amplitudes and is less steep. Phase also shifts to later times of day moving inland from the coast, with most grid boxes over land having a late afternoon to evening phase. As observed over ocean, ERA5 produces a similar change in phase to IMERG and Ku radar but shifted 1–2 h earlier. Over land, the amplitude pattern with distance from shore is different between the three datasets, with IMERG and Ku radar showing a slight increase with distance from shore, and ERA5 showing a strong decrease.

Precipitation over Amazonia propagates westward from the Atlantic coast in an approximately 2-day cycle, as observed by IMERG and Ku radar. The westward propagating line meets a line propagating eastward from the Andes in the eastern portion of the region. ERA5 does not produce much propagation over land, instead showing an early afternoon phase over much of the region. A steady decrease in amplitude with distance from shore is shown in all three datasets until ∼500 km from the Atlantic coast. Amplitude then tends to remain fairly constant with distance from shore, with IMERG and Ku radar showing a wide range of amplitudes at these distances. As in the Maritime Continent, ERA5 produces a much lower range of amplitudes over Amazonia, with amplitudes near the coast being especially muted.

The diurnal cycles over the Maritime Continent and Amazonia are complicated and strongly influenced by local properties. This makes high-resolution precipitation datasets such as IMERG important tools for studying the precipitation in these regions, though its limitations revealed here by the comparison to the Ku radar observations should be noted. The wide range of local processes influencing the precipitation diurnal cycle in these regions also makes them well suited to describing the similarities and differences in commonly used precipitation products, illuminating the strengths and weaknesses of each. Future work on this topic would include additional regions, in order to study the effects of different diurnal forcings, separating the analysis by precipitation type, and a seasonal analysis, especially in the Amazon region, where the ability of the sea breeze disturbance to propagate inland may be linked to the favorability of the environment for convection.

Acknowledgments.

Thanks to the Support for Atmospheres, Modeling, and Data Assimilation (SAMDA) and Science Systems and Applications Inc. (SSAI) for the internship supporting the first author throughout the beginning of this work. All authors have also been supported throughout the majority of this work by NASA Precipitation Measurement Mission funding. Special thanks to Chuntao Liu for processing assistance and for the use of his computing resources.

Data availability statement.

IMERG, GPM DPR, and TRMM PR data are available for download from NASA at https://gpm.nasa.gov/data/directory. ERA5 hourly precipitation data are available for download at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview.

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