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What Drives Daily Precipitation Over Central Amazon? Differences Observed Between Wet and Dry Seasons

. This study suggests an alternative approach on how diurnal precipitation is modulated by the nighttime events developed over Central Amazon using data from the Observations and Modelling of the Green Ocean Amazon (GoAmazon 2014/5) field campaign in the Central Amazon as well as radar and satellite data. Local observations of cloud occurrences, 10 soil temperature, surface fluxes, and planetary boundary layer characteristics are coupled with satellite data to identify physical mechanisms that control the diurnal rainfall in Amazonas during the wet and dry season. This is accomplished by evaluating the atmospheric properties during the nocturnal periods from the days prior to rainfall and non-raining events. Comparisons between non-rainy and rainy transitions are presented for the wet (January to April) and dry (June to September) seasons. The results suggest that wet season diurnal precipitation is modulated mainly by night-time cloud 15 coverage and local effects such as heat- induced turbulence, while dry season rain events are mainly controlled by large-meso scale circulation. and its surrounding did not suffer any deforestation or change in its surface coverage over the 2 year period of our analysis. Furthermore, radar analysis provided by Dr. Die Wang from the Brookhaven National Laboratory shown that precipitation is enhanced southwest from the SIPAM radar, regardless of river proximity. Therefore, we define “local influences” here as 120 influences within a few kilometers around the site, occurring in this area between the rivers, which could be represented in a typical GCM grid.


Classification of Raining and Non-Raining Events 80
In the Amazon, convection typically initiates around noon and precipitation presents its maxima around 14 LT (Machado et al., 2002). We define a previous day 'nocturnal period' as the period between 00 GMT and 12 GMT (20 LT to 08 LT), and the following 'diurnal period' as the period between 12 GMT and 00GMT (08 LT to 20 LT). The diurnal period starts roughly 2 hours after the sunrise in Manacapuru, which occurs around 06 LT and does not vary much throughout the year.
To understand what controls the convection for the daytime period that follows, we categorize Amazon observations into 85 two classes: a) days having no rain during the nocturnal period and no rain during the subsequent diurnal period; and b) days having no rain during the nocturnal period, but observing rain during this subsequent diurnal period. We refer to these transitions as NR-NR (No Rain to No Rain) and NR-RR (No Rain to Rain), which comprise separate 24-hour events. Our intention is to identify the possible controls during nocturnal periods that may initiate precipitation in the subsequent diurnal window. For completeness, days recording rain within the nocturnal period (39% of the days during the wet season, and 9% 90 during the dry season) are not considered for this analysis, as we are only interested in diurnal cloud cycling and the onset of precipitation. The choice of a 12-hour nocturnal period having no precipitation also provides an additional control, since daytime convection having precipitation is frequent, and especially during the wet season. Thus, we do not assume that convection is only dependent on nocturnal conditions but expanding such periods to a full (prior) day conditions would often result in precipitation observations. 95 We used local weather station datasets at T3 as well as gridded SIPAM radar datasets (1 km-horizontally gridded, 3 km-level constant altitude plan position indicator (CAPPI))) to define precipitation events. The SIPAM monitoring of the area was done to include days where local/column observations would otherwise miss reasonable rainfall events. A 50 x 50 km area (similar to a typical GCM gridbox resolution) centered at the T3 site was used, and gridded radar reflectivity factor values greater than 25 dBZ in this domain were considered as 'precipitation' echoes. If more than 10% of the area was covered by 100 such reflectivity values greater than 25 dBZ at any time during any given hour, or if the local weather station reported a rainfall accumulation greater than 1 mm during that same hour, then we defined the day as a rain (NR-RR) event.
For this study, we defined the 'wet' season as the period between January and April and the 'dry' season as between June and September (Giangrande et al., 2017, Machado et al., 2018. To ensure sufficient sampling, we used the entire GoAmazon2014/5 record (2014GoAmazon2014/5 record ( and 2015 in our analysis and did not attempt to differentiate year-to-year GoAmazon2014/5 105 dataset variability, such as the case in previous Amazon efforts (e.g., Jiménez-Muñoz et al., 2016). Following our definitions, we identified 51 NR-NR cases and 113 NR-RR cases during the wet season. The dry season showed a reversal in event frequency, with 148 NR-NR cases and 64 NR-RR cases. These cases were distributed throughout the campaign as presented in Figure 1. No intra-seasonal variability is observed in those distributions, however an ENSO event of 2015 is pronounced (e.g., Jiménez-Muñoz et al., 2016), as suggested by the larger number of NR-NR cases during the dry season of 110 2015 (Figure 1 (d)). Also, NR-RR days with an active Kelvin wave mode were only found for 7% of this wet season dataset (not shown, a classification of Kelvin wave activity was kindly provided by Dr. Yolande Serra from the Joint Institute for the Study of the Atmosphere and Ocean -University of Washington).
Possible river-breeze or other land contrasts influences in the rainfall distribution are expected, but they are not object of this study. Land-breeze effects are known to enhance the nocturnal rainfall in near-river areas (Cohen et al., 2014;Fitzjarrald et 115 al., 2008) and affect local low-level circulation in near-river areas (de Oliveira and Fitzjarrald, 1993). All of our data was obtained over the same site (T3), located 10 km from the Solimoes and 25 km from the Negro river. The site and its surrounding did not suffer any deforestation or change in its surface coverage over the 2 year period of our analysis.
Furthermore, radar analysis provided by Dr. Die Wang from the Brookhaven National Laboratory shown that precipitation is enhanced southwest from the SIPAM radar, regardless of river proximity. Therefore, we define "local influences" here as 120 influences within a few kilometers around the site, occurring in this area between the rivers, which could be represented in a typical GCM grid. https://doi.org/10.5194/acp-2020-1098 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License.

Low cloud diurnal cycle from cloud radar
In Figure 2, we display ARSCL-derived mean cloud fraction values. This represents the average fraction of time when a cloud was observed over the site during each observation period, and for the various rain regime separations (i.e., pairings for wet/dry season breakdowns, and NR-NR/NR-RR modes). Initial emphasis is on the lower portion of the atmosphere below the freezing level (approximately 4.5 km) since we anticipate shallow clouds during non-raining nocturnal periods. 130 Note, residual cirrus from the previous day deeper cumulus clouds (or advected into the domain from distance) may be anticipated, however these upper level clouds are not the focus of this analysis. In the bottom panels of Figure 2, we plot the absolute cloud occurrence difference between the modes for the wet and dry seasons, respectively. The local time axes on these images has been extended to 12 LT to better illustrate the onset of convection (or lack thereof) in our mode and difference properties. 135 Consulting wet season properties along the leftmost panels, one can note that the NR-NR transition (Figure 2 (a)) presents higher cloud coverage during the night than the NR-RR mode (Figure 2 (b)). The NR-NR mode features cloud occurrences above 20% from 22 LT to 04 LT between 1 and 3 km, and from 00 LT and 04LT in several cases. In addition, the NR-NR mode suggests an earlier onset for shallow clouds than the NR-RR transition days, and the near-surface occurrences are higher than 20% most of the times from 00 LT to 04 LT. These near surface shallow clouds may be attributed to fog, 140 frequently observed from midnight to noon during the wet season (Anber et al., 2015, Giangrande et al., 2020. From sunrise until 10 LT, the NR-RR mode also suggests low-level cloud activity, possibly related to fog occurrence as well. During the late morning, the NR-NR mode indicates a high frequency of shallow convection after 10 LT, with cloud occurrences exceeding 45% confined to a shallow layer around 1 km altitude. During the raining transition the 1 km-layer cloud coverage is generally lower compared to the NR-NR mode, where before 06 LT cloud occurrence rarely exceeds 15%. The shallow 145 convective activity observed at sunrise is weaker than the wet NR-NR mode, but after 10 LT cloud occurrence exceeds 45% and its 30% contour height reaches 2.5 km. Note, since we extend our period of study from 08 LT to 12 LT, there will be raining clouds contaminating these results after 08 LT, for all transition modes and seasons. The absolute difference ( Figure   2 (c)) shows that for most times from 22 LT to 04 LT, the non-raining mode has higher cloud occurrence, especially below 6 km, with differences around 20%, which happens also frequently in higher levels between 20 LT and 22 LT. After 10 LT the 150 NR-RR mode shows the maximum negative cloud differences, reaching -20%.
A physical interpretation of the wet season results is that this higher cloud occurrence in the NR-NR mode during the nocturnal periods would imply additional consumption of energy that might have been available for convection during the following daytime period. One question is whether these night-time clouds could be formed by radiative cooling from the top of the boundary layer, thus not associated with consumption of CAPE. However, CAPE and CIN observations 155 (subsequent section) indicate these thermodynamic parameters are reduced for these NR-NR modes. In addition, cloud coverage during early mornings (frequency over 25% observed between 06 and 07 LT near the surface and at 3 km AGL) would limit surface heating, e.g., reduced incoming solar radiation. Alternatively, an increase in the incidence of solar radiation during the NR-RR mode associated with reduced cloud coverage generates surface heating that would favor subsequent daytime convective development. This behavior was discussed from an energy budget standpoint by Machado 160 (2000), where it was shown that the surface loses more energy than it receives in convective events, therefore reduced energy is available at the surface following a cloudy period. Using observations over the Amazon, they show that the surface https://doi.org/10.5194/acp-2020-1098 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License. absorption of solar energy was always smaller (larger) than the total surface flux provided to the atmosphere throughout convective (non-convective) events. The quantity of energy stored at the surface seemed to be constrained, and defines a timescale, during which the surface needs to export or receive energy to stabilize its deficit or gain of energy. Beginning at 165 06 LT the differences throughout the whole column (except close to surface between 06 and 07 LT) favor the NR-RR mode.
The dry season results illustrate most of the cloud activity during the night-time window occurs at the higher cloud levels (i.e., above 7 km, and provides a strong contrast with the increased lower cloud coverage observed during the wet season (e.g., between 0 and 6 km). The NR-NR mode (Figure 2 (d)) suggests reduced cloud occurrence (e.g., frequency values less than 15%) during the night-time hours. The NR-RR cases (Figure 2 (e)) suggest increasing cloud coverage above 1 km and 170 additional near-surface/low clouds after 08 LT, which contrasts with the NR-NR modes that suggest low-cloud occurrence below 5%. The difference field for the dry season ( Figure 2 (f)) implies that the raining mode is predominantly cloudier than the NR-NR mode. During the nocturnal period, the maximum difference in occurrence falls between an 8% to 12% increase in the favor of the NR-RR mode (here, for the levels between 2 and 4 km). Thus, these dry season non-rain to rainy differences are reduced (in absolute value) when compared to the wet season behaviors. A physical interpretation for these 175 dry season behaviors will be discussed in section 4.2.

Radiosonde analysis
In Figure 3, we present statistics for the thermodynamic parameters CAPE and CIN using data derived from the nocturnal (20, 02, and 08 LT) radiosondes launched at T3. The boxplots were constructed to display the minimum, lower quartile, 180 median, upper quartile, and maximum values.
As previously introduced, the wet season CAPE results (Figure 3 (a)) suggest a reduction of the potential energy from 20 to 02 LT during the NR-NR transition (grey boxes), whereas the NR-RR mode results (blue boxes) are similar for these two measurements. Recall, a physical explanation for this reduction in CAPE between the two first observations (20 and 02 LT) is convective cloud energy consumption, since a cloudier condition is argued between 22 and 02 LT for these NR-NR 185 transition events. The NR-NR 20 LT CAPE is the highest magnitude/distribution we observe, suggestive for an eventual increase of the cloud coverage, which in turn consumes this energy, but ultimately decreasing the CAPE by the measurements of the subsequent sounding. Between 02 and 08 LT, we observe an increase in the distributions for CAPE for NR-NR and NR-RR modes during the wet season. This increase is suggested as owing to the surface heating and the increase of the surface temperature after the sunrise. CAPE values are observed higher for the NR-RR mode by the timing of 190 the 02 LT and morning 08 LT radiosondes when contrasted with the NR-NR mode composites. By 20 LT (typically, after daytime rainfall onset), the non-raining mode still indicates the largest upper quartile value and maximum CAPE values, albeit the medians are nearly identical between NR-RR and NR-RR modes.
The dry season (Figure 3 (b)) plots indicate higher CAPE values at the morning 08 LT radiosonde times when compared to the wet season for each mode we present. These results are compatible with the higher soil temperature (and overall reduced 195 precipitation, surface moisture) observed during the dry season. The energy decrease in the NR-NR mode between 20 and 02 LT is present, yet less pronounced than that observed during the wet season. The NR-RR changes observed between 20 and 02 LT are subtle: a slight increase of the upper quartile value and a decrease of the maximum value. An explanation for the similarities between the 20 and 02 LT results, for both modes, and their differences in comparison with the wet season results, is the reduced cloud coverage (overall). Moreover, reduced or less favorable cloud coverage, as observed during the 200 dry season, implies a lower convective activity overall. The 02 LT sounding values are similar for the dry and wet seasons, for both NR-NR and NR-RR transitions. The wet season CIN (Figure 3 (c)) shows that the convective inhibition is less intense than those observed during the dry season (Figure 3  Finally, a t-Student test was applied to this dataset. These efforts found that the differences between the modes were 205 significant at the 0.05 level for the 02 and 08 LT results above. However, the 20 LT observations were not found to meet these significance criteria. These statements include both the CAPE and CIN behaviors, and the behaviors for wet and dry seasons. Overall, these results may be somewhat expected, but these properties are presented to demonstrate a strong consistency with behaviors discussed for the NR-NR versus NR-RR modes. The composite radiosondes are presented in Figure 4 (02 LT composite) and Figure 5 (08 LT composite). Left panels 210 indicate wet season observations, solid lines are NR-RR data, and dashed lines are NR-NR data. As one might expect, the dry season composites are much drier than their wet season. One feature that can be noticed in these composites is higher temperatures close to the surface in the 02 LT data than those observed in the 08 LT sounding, corroborating with the surface temperature observations (not shown) that presented higher temperatures from 02 LT up to 08 LT. The dry temperature profiles present subtle differences between NR-RR and NR-NR modes during the dry season, and they are almost the same 215 for the wet season. Rain/no-rain differences in the dew point temperature profiles are more pronounced than those observed in the dry temperature profiles (especially during the dry season). There is evidence that dry season precipitation is linked to larger-scale moisture advection, as we will discuss in section 4.1.5.

Sensible and latent heat fluxes analysis
Cloud coverage directly impacts the incoming solar radiation by changing the Earth-system albedo. A higher (lower) cloud 220 coverage will imply in less (more) incident solar radiation reaching the surface, altering the sensible and latent heat flux balance. As the surface heats up, thermally induced turbulence is produced, via convection. As presented in Figure 2, the wet season NR-RR mode presents lower cloud occurrences up to 1 hour after the sunrise, when the magnitude of both sensible and latent heat fluxes begin to grow. To examine the relationships between cloud coverage and surface fluxes, we present the mean latent heat flux and the mean sensible heat flux measured by the ECOR system, in Figure 6  NR-RR modes: we observe higher flux values during the NR-RR modes up to 08 LT, and after 08 LT, with the onset of precipitation, temperature drops and the differences between the NR-RR and NR-NR fluxes becomes negative. The fluxes analysis seems to corroborate the local cloud coverage results (Figure 2), since dry season fluxes are statistically the same (when looking at low cloud occurrence differences) and during the wet season the NR-NR fluxes are lower, when we notice 235 more cloud coverage during the NR-NR mode, reducing the incoming solar radiation and therefore surface heating in comparison with the NR-RR mode. This analysis also indicates the role of the surface moisture in the PBL development. The higher soil moisture in the wet season may lower the Bowen ratio, favoring latent heat and developing more clouds, thus lowering the PBL (Giangrande et al., 2020), as we discuss in the next sections.

Planetary boundary layer analysis 240
The planet boundary layer over the Amazon was subject of study of past field-campaigns, such as the Amazon Boundary-Layer Experiment -ABLE2a and ABLE2b (Harriss et al., 1988;Garstang et al., 1990) and also during the Large Scale Biosphere-Atmosphere Experiment -LBA (Silva Dias et al., 2002). Studies such as Martin et al., 1988, andFisch et al., 2004, described the characteristics and evolution of the PBL over the Amazon during these experiments. The depth of the mixed layer below cloud base as well as the relative humidity near surface and the lifting condensation level are tightly 245 https://doi.org/10.5194/acp-2020-1098 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License. coupled in the diurnal convective boundary layer over the Amazon (Betts et al., 2006). The composite dataset planetary boundary layer heights properties for the various modes and seasons as estimated using ceilometer are plotted in Figure 8. As already demonstrated in previous studies (e.g., Betts et al., 2002Betts et al., , 2013, the wet season, being the season with most convective precipitation activity overall, presents lower PBL heights. As from Figure 8, the observed PBL heights during the dry season are higher than the wet season ones, and even the NR-RR mode of the dry season has a higher PBL than the wet 250 season NR-NR mode. During the wet season, the distinction between the NR-NR and NR-RR transitions begins to appear at 08 LT. The maximum PBL height (approximately 1000 m) is reached around local noon for the NR-RR transition, whereas the NR-NR maximum is 500 m higher and reached 2 hours later. Both height and time differences observed can be explained by the more frequent convective development that occurs the wet season. With moisture freely available during the wet season, any conditional instability that favors cloud development such as surface heating or local instabilities can trigger 255 convection, thus lowering the PBL. Carneiro (2018) and Carneiro et al. (2020), using observational data from ceilometer, LIDAR, and LES simulations shown that the erosion of the nocturnal boundary layer occurs 2 hours after the sunrise during the dry season and 3 hours after the sunrise during the wet season.
The normalized hourly rainfall occurrence distribution (Figure 7) suggest that the precipitation occurrences are more distributed over the daytime window during the wet season, while the dry season distribution indicates a distinct peak around 260 noon. In total, 502 events were observed in the wet season and 192 events were observed in the dry season during this GoAmazon2014/5 period. The seasonal differences between the diurnal cycles of the rainfall occurrence may help explain the contrasts observed between the PBL heights. In the dry season, one third of the rainfall occurrences is observed between 12 and 14 LT, which corresponds to the time when the NR-RR and NR-NR PBL heights begin to present a more prominent difference, in contrast with the wet season, where the PBL heights are different from 08 LT. 265 The ECOR derived turbulent kinetic energy (TKE) results are presented in Figure 10. TKE was measured at 3 m from surface, and although flights were available during the campaign, they were scarce (less than 20 flights per IOP), not allowing us to use the data for a statistical analysis. Results show that the dry season presents higher values of TKE than the wet season with few differences between the modes. However, clear differences between the wet season modes are observed, with the NR-RR mode having the higher values of TKE, reaching 1.2 m -2 s -2 around the local noon. The NR-NR and NR-RR 270 wet season curves show significantly different values after 10 LT, indicating a more turbulent low-level atmosphere in the presence of rain during the wet season. During the wet season, the raining mode presents higher TKE values overall. Before the onset of convection, this behavior can be related to larger surface heat fluxes (presented in Figures 4 and 5, right panels) from 06 to 09 LT. In the presence of rain, the larger TKE values can be explained by turbulence generated by stronger winds observed during rain cell events. Oppositely, the dry season TKE is similar for both NR-NR and NR-RR modes. The similar 275 magnitudes may be an indication of drier soil conditions, overall absence of shallow clouds, an indication of higher temperatures during both modes, or some combinations therein to be discussed below, but during the wet season the difference is highlighting the importance of local cloud processes in the rainfall events.
Surface temperature also plays an important role on the TKE, since higher surface temperatures will increase thermal turbulence and near-surface wind speed (Jacobson, 2005). On the TKE results (Figure 10), we observe that TKE is lower 280 during the wet season, for both modes, which can be a response to the lower temperatures observed in this period, as we present in Figure 11. The wet season temperatures (Figure 11, left panel) presents higher differences between NR-NR and NR-RR modes beginning at 12 LT, due to surface cooling caused by rainfall beginning at 08 LT. Dry season temperatures ( Figure 11, right panel) are similar for both modes, which is an indication that the temperature does not change as much during rain events in comparison with the wet season, and helps explain the similarity between NR-NR and NR-RR dry 285 season TKE curves. Also, in the dry season, one might anticipate a drier soil (resulting in higher Bowen ratios), and a drier boundary layer (less clouds), implying in a stronger generation of turbulent boundary layer growth (Giangrande et al., 2020, Jones andBrusnell, 2009). https://doi.org/10.5194/acp-2020-1098 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License.

Local observationssummary
The results presented in the previous sections indicate that the precipitation onset in the dry season is weakly correlated with local factors. However, the local ARM site observations presentedcloud fraction, fluxes, CAPE/CIN, PBL characteristics, surface temperatures, and turbulenceshow distinct features between non-raining and raining transitions during the wet season, with CAPE/CIN also showing a significant difference between rain and non-rain modes during the dry season. Also, 295 these features are correlated: soil temperature and TKE presents the same NR-NR/NR-RR difference characteristics, as well as PBL height and rainfall, surface fluxes and cloud coverage. Although the dry season analysis presented similar characteristics between raining and non-rain modes, (Ghate and Kollias, 2016) state that during the dry season local landatmosphere interactions may trigger the transition from shallow to deeper convection, and indicate a relationship between large-scale moisture advection and precipitation. We did not find evidence for local interactions being responsible for dry 300 season diurnal precipitation, nor we analyzed moisture advection, but we present a large-mesoscale cloud analysis in the next section.

Large-mesoscale analysis
To further investigate the influences of local effects versus the influence of the mesoscale cloud patterns from the nocturnal period on the subsequent rain transitions, we calculated the mean field of the GOES 10.4 μm brightness temperatures 305 observed over a 10° x 10° box centered at T3 during the nocturnal period. In addition, we calculated the cumulative distribution function (CDF) and the probability distribution function (PDF) of these brightness temperatures, grouped in 3h intervals and separated by transition type and season. Mean brightness temperature fields during the nocturnal period (20 LT -08 LT) observed over a 10° x 10° box centered at T3 (the cross mark in each panel) are presented in Figure 12. These are shown for NR-NR and NR-RR modes (top and middle panels), and for wet/dry season breakdowns (left and right columns, 310 respectively), with absolute differences presented on the bottom panel. These brightness temperature fields can be related to the cloud occurrence fields from Figure 2, as higher cloud occurrences will result in lower brightness temperature means.
Anticipated differences (as from ARSCL properties in Figure 2) in convective activity between NR-NR and NR-RR transitions during wet and dry seasons are hold in these spatial cloud field representations. For example, convection is more intense during the wet season (Figure 12 (a) and (b)), and it is observed around the whole domain. Temperatures below 275 315 K can be observed in more than 90% of the region for both transition types. Roughly 81% of the differences between NR-NR and NR-RR modes are statistically non-significative (Figure 12 (c)). The differences among the two transition modes in the wet season is related to the terrain: the regions in the north and southwest of the domain, that presents the main differences, are areas where there the dominant wind flow (from northeast) are lifted over areas where the terrain elevation rises (Figure 13). 320 During the dry season, the NR-NR transition ( (Figure 12 (d)) is associated with warmer temperatures, and values above 280 K comprise almost all the region. The NR-RR transition (Figure 12 (e)) suggests colder temperatures, 5 to 10 K lower than the NR-NR transition overall. Approximately 72% of the temperature differences between the non-raining and raining mode are found between 8 and 20 K (Figure 12 (f)). This feature strongly suggests that the large-scale cloud conditions during the dry season are very different between raining and non-raining days. 325 The mean behavior observed in Figure 12 can be detailed breaking the observations in fixed time intervals. In Figure 14, we present the PDF of the GOES-13 10.4 μm brightness temperatures grouped into 3 h time steps over the nocturnal period.
This breakdown helps diagram the evolution of the convective systems around the T3 site and identify the differences presented between seasons and transitions therein. All distributions plotted in Figure 14 are left-skewed unimodal https://doi.org/10.5194/acp-2020-1098 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License. distributions, with peaks between 285 K and 295 K. Wet season distributions (dashed lines, both modes) are similar for both 330 transition modes and for all time intervals considered. Values observed for the wet season are generally lower (colder temperatures) than those observed in the dry season, indicating a stronger convective activity throughout the domain independent of transition type or time interval. Dry season distributions (solid lines, both modes) are quite different during NR-NR (black lines) and NR-RR (red lines) events, with a larger incidence of higher values (e.g., warmer temperatures or absence of higher clouds) during NR-NR transitions. 335 The wet season mean cloud field similarities are better illustrated in the CDFs (Figure 15), where these CDFs indicate that the wet season mean cloud field does not change as much as the dry season distributions during the overnight window, regardless of the precipitation observed during the subsequent day. In other words, the wet season large-meso scale mean convective characteristics have approximate the same characteristics for both transition modes, and the development of precipitating clouds observed at T3 during the wet season appears to be influenced mostly by local factors, as discussed 340 before. In contrast, the dry season distributions (solid lines) are quite different: the lower quartile (Q1) value of the NR-RR transitions (red lines) is often reached around 250 K, while for the NR-NR transitions (black lines) the Q1 value lies around 280 K. The NR-NR CDFs are very similar for all time intervals, but the NR-RR CDFs change with the time, and the differences between them increases as time passes. The dry season NR-RR curves also suggest colder values than the wet season curves from 23 LT onwards, which implies that when precipitating convection happens during the dry season, these 345 clouds tends to be stronger/deeper than those in the wet season. This finding for intense dry season convection is consistent with several previous studies (e.g.: Itterly et al., 2016;Tanaka et al., 2014). Overall, the difference between the wet/dry seasons and the results presented in section 4.1 (with local observations) suggest that for the dry season, precipitation is controlled directly by large-meso scale circulation and local effects are less important. In contrast, the wet season suggests that local processes are more of the dominant factor in the night-time hours preceding the next days' diurnal rainfall. 350

Conclusions
In this paper, we present an alternative take on how daily precipitation is controlled by the night-time events over the Central Amazon. We break down our results based on seasonwet and dryand 2 modes of transition: non-raining evenings to non-raining days and non-raining evenings to raining days. The results suggest that during the wet season, local influences are the key driver of rainfall occurrence over our region of study. During the dry season, mesoscale-large scale factors are 355 more important and dominate the development of the precipitation observed.
Moreover, precipitating cloud development is strongly associated with moisture availability, and boundary layer vertical motions or turbulence. We propose that during the wet season, when moisture levels observed are higher, cloud development is a direct effect of the local vertical motions. During the dry season, with moisture being less available and most of the incident solar radiation being converted to sensible heating, precipitating clouds are driven by large-meso scale circulation. 360 The results presented here suggest that during the wet season the diurnal precipitation is modulated mainly by the cloud coverage during overnight hours. Since cloud development is associated with vertical motion and moisture availability, and since during the wet season moisture is freely available, we speculate that the local scale nocturnal vertical motion is responsible for the cloud development. Therefore, the wet season NR-RR transition has a weaker upward vertical motion during the night (from 22 to 04 LT) and immediately following the sunrise (from 06 to 07 LT), reducing cloud formation 365 during the first hours of the morning. This allows the surface to receive more solar energy, favoring instability. This is supported by the soil temperature and turbulent kinetic energy observations during the diurnal period. Since there is enough moisture available, we hypothesize that heating is transformed into latent heating, building convective cells that will precipitate later during the day. However, in the wet season NR-NR mode, nights with dominant shallow convection will reduce convection during the day, because the clouds formed during the night will reduce solar radiation at the surface 370 https://doi.org/10.5194/acp-2020-1098 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4.0 License. during the first hours of the day. During the wet season, a clear distinction during the night is observed between the NR-NR and NR-RR days, but for the dry season no significant signal is observed. This indicates that the local processes are not the key feature controlling the transition from shallow convection to rainfall between June and September. Previous studies (D'Almeida et al., 2007;Khanna et al., 2018;Lawrence and Vandecar, 2014) show that contrasts in land occupation (e.g.: forest and pasture, forest and deforested areas) have more impact on atmospheric and hydrological properties -PBL 375 development, precipitation etc. during the dry season. These studies were performed measuring surface and atmospheric properties over different surfaces, showing that land occupation contrasts cause local circulations that can trigger convection and rainfall more often in the dry season. Here, we do not verify such land occupation contrasts, since all of our data was obtained over the same site (T3), and the site and its surrounding did not suffer any deforestation or change in its surface coverage over the 2 year period of our analysis. 380 The PBL analysis indicates that thermal turbulence does not play a major role on cloud formation during the dry seasonthere are no distinguishable differences between the NR-NR and the NR-RR transitions. Alternatively, the distinction between the transitions are clear during the wet seasonboth the turbulent kinetic energy and PBL heights have different values between raining and non-raining modes. TKE observations are corroborated by the soil temperature measurements, showing a connection between seasonal and rain-induced temperature differences and TKE observed. 385 In addition, the satellite data analysis shows that during the dry season precipitation is observed at T3 during days where cloud activity is seen throughout all its surroundings during the overnight hours, indicating a large-meso scale modulation in the convection during this season. There is a clear difference in the PDFs and CDFs between the raining modes. However, wet season brightness temperatures distributions are similar for NR-NR and NR-RR transitions. Statistically significant differences between NR-NR and NR-RR modes during the wet season are less frequent than those observed in the wet 390 season, indicating that rainfall modulation during the wet season is less affected by the large-scale cloud background.
In summary, these results imply that models and parameterizations must consider different formulations based on the seasonal cycle to correctly resolve the precipitating convection over central Amazon. A convective parameterization scheme using only local or small-scale interactions will give poor results during the dry season. On the other hand, larger mass-flux convergence approaches will not perform well during the wet season, triggering precipitation at wrong times or quantifying 395 it erroneously. Parameterizations schemes must consider seasonal differences in their formulation, and as already pointed out in several studies (D'Andrea et al., 2014;Gentine et al., 2013 and references therein), unified PBL/shallow convection/deep convection parametrization schemes present the better option to correct representation of the rainfall diurnal cycle.

Data availability
All ARM datasets used for this study can be downloaded at http://www.arm.gov and are associated with several "value 400 added" product streams.

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Solid lines are NR-RR data, and dashed lines are NR-NR data. The red line is dry temperature and green the dewpoint temperature.