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During the pre-summer rainy season, the precipitation intensity increases gradually over SC from April to June; it is about 3–9 mm d−1 in April, 7–13 mm d−1 in May, and 9–15 mm d−1 in June (Fig. 1). The four precipitation datasets show a very similar distribution in each month, and TMPA 3B42 has the highest spatial correlation coefficients with STN in each month (0.95, 0.89, 0.90; Table 1). Specifically, there are two major precipitation centers in SC that are located in northern Guangxi Province and over the southeast coast–northern Pearl River Delta, respectively. According to the spatial distribution of daily mean precipitation in each month over SC, the area with large values (21°–26°N, 108°–117°E) was selected as the core region in this study. It should be mentioned that the relatively large area means that some locally occurring warm-sector extreme rainfall events were excluded in our study. These cases always happen suddenly and are usually characterized by complicated boundary layer triggering mechanisms, complex topography, land-sea thermal contrasts, and especially, weaker synoptic scale baroclinic forcing (He et al. 2016). Based on the 95th percentile of daily precipitation series (rainy days), the criteria of EPEs for the station, TMPA, CMORPH-CRT and CMORPH-BLD data are 27.0, 28.0, 24.0 and 24.0 mm d−1, respectively (Table 2). Following the methods described in section 2.2, year-by-year pre-summer rainy season precipitation power spectra averaged over SC were computed for the four datasets (figures not shown). Four dominant frequency bands were identified in the 21-year study period—namely, 3–8 d, 10–20 d, 15–40 d, and 20–60 d. As an intuitive example, Fig. 2 integrates the power spectra and period distributions of the TMPA data for all 21 years. Clearly, the 3–8-d high-frequency synoptic mode is the most significant period in each of the 21 years, followed by the QBW, ISO and QM modes. Moreover, the results were qualitatively consistent when the other three datasets were used (not shown).
Figure 1. Spatial distribution of the temporally averaged station, TMPA 3B42, CMORPH-CRT and CMORPH-BLD daily precipitation rates (mm d−1) over China for (a, d, g, j) April, (b, e, h, k) May, and (c, f, i, l) June of 1998–2018. The inset box denotes the core region of SC (21°–26°N, 108°–117°E), which is the same in subsequent figures.
Datasets April May June TMPA 3B42 vs. STN 0.95 0.89 0.90 CMORPH-CRT vs. STN 0.80 0.71 0.64 CMORPH-BLD vs. STN 0.90 0.79 0.79 Table 1. The spatial correlation coefficients (significant at 99% Student’s t-test) between monthly averaged rainfall of STN and TMPA 3B42, CMORPH-CRT, CMORPH-BLD for 63 stations over SC. The grid datasets are interpolated to stations.
Dataset EPE Synoptic (3–8-d) QBW (10–20-d) QM (15–40-d) ISO (20–60-d) STN 27.0 5.4 3.6 3.4 3.1 TMPA 3B42 28.0 5.3 3.8 3.7 3.4 CMORPH-CRT 24.0 4.8 3.4 3.3 3.0 CMORPH-BLD 24.0 4.9 3.4 3.2 3.0 Table 2. EPE thresholds and one standard deviation of bandpass-filtered precipitation (mm d−1) in each frequency band for the four datasets over the core region of SC.
Figure 2. The year-by-year distribution of the pre-summer rainy season precipitation power spectra averaged over SC during 1998–2018. The filled values are log values of the computed power spectrum divided by the corresponding Markov red-noise spectrum (log values equal to or larger than 0.0 mean that the computed spectrum is significant). The abscissa is period in days. The four dominant frequency bands are indicated by the dashed lines with different colors.
Apart from the highest spatial correlation coefficients between TMPA 3B42 and STN in each month, it is apparent that the EPE criterion and one standard deviation of bandpass-filtered precipitation at the synoptic and QBW scales of TMPA are quite similar to those of the station observations (with differences of 0.1–0.2 mm d−1), but the values of CMORPH-CRT and CMORPH-BLD are obviously underestimated (with differences of 0.2–0.6 mm d−1) (Table 2). In addition, CMORPH-CRT and CMORPH-BLD show low (discontinuous) precipitation areas near the Pearl River Delta (Figs. 1g–l), which was also found in Huang et al. (2018) and noted by other Chinese scientists (personal communication). Considering the above two factors, the CMORPH data are not analyzed further. The TMPA precipitation is highly consistent with that of the station observations over the Chinese mainland and covers the ocean surface where EPEs often happen. Therefore, only the TMPA data are employed in the remainder of the paper.
Based on the spectral peaks (i.e., the filled values equal to or greater than 0.0 in Fig. 2) of precipitation in each year, bandpass-filtered precipitation anomalies and raw precipitation time series were compared to select EPEs and corresponding dominant frequency modes (Fig. 3). Here, the extreme rainfall days associated with tropical cyclones are excluded (e.g., three cases in 2018). To ensure the reliability of the analysis, the heavy rainfall should also exist in the other three datasets on the same day. EPE activity exhibits obvious interannual variations, e.g., six cases in 1998 and none in 2017 and 2018 (two EPEs in June were affected by typhoons), with three to four cases occurring in most years (Table 3, Fig. 3). We find that EPEs are most active in May and less active in April. In the pre-summer seasons of the 21-year study period, there are a total of 67 EPEs, and each EPE has one (synoptic) or two (synoptic and QBW/QM/ISO) dominant frequency modes (Tables 3 and 4). Among them, the 3–8-d single synoptic mode has the largest number (25) of cases (37%). However, the case numbers for the 3–8-d & 10–20-d, 3–8-d & 15–40-d, and 3–8-d & 20–60-d multiscale combined modes are far higher than the single synoptic modes. In addition to holding the majority in terms of numbers, the peak precipitation intensities of the multiscale combined modes are also somewhat larger (~36.0 mm d−1) than those of the single synoptic modes (33.5 mm d−1) (Table 4). Figure 4 presents the composite results for four types of EPEs. The peak precipitation days coincide well with the peaks of each bandpass-filtered positive rainfall anomaly. As in Liu et al. (2014), the phase analysis method was adopted to identify the life cycle of each EPE, as well as the spatial structures and temporal evolution of the precipitating disturbances. The peak dry phase (phase 1, P1) and peak wet phase (phase 5, P5) refer to the minimum and maximum precipitation day at each time scale, respectively (Fig. 4). Obviously, the precipitation intensity on peak wet days is far more intense than on other days in all four EPE types.
Figure 3. Time series of raw daily precipitation rates (gray bars; mm d−1; left y-axis), bandpass-filtered precipitation anomalies (red: 10–20-d; orange: 15–40-d; green: 20–60-d; black: 3–8-d; mm d−1; right y-axis) during the period 1 April to 30 June 1998–2018. The x axis represents time period of April to June in each year. The years are classified into four groups based on the dominant frequency bands in each year: (a) 3–8-d, 10–20-d, (b) 3–8-d, 15–40-d; (c) 3–8-d, 20–60-d; (d) 3–8-d, 10–20-d and 20–60-d. Solid pink lines represent the extreme precipitation intensity over SC; dashed pink lines represent one standard deviation of bandpass-filtered precipitation in each frequency band. The symbols ▽, ☆, ✡ ◇and represent EPEs accompanied by significant 3–8-d, 3–8-d & 10–20-d, 3–8-d & 15–40-d, and 3–8-d & 20–60-d disturbances, respectively.
Synoptic Synoptic&QBW Synoptic&QM Synoptic&ISO 19980502 19990526 19980427 20020514 19980523 19990623 19980516 20050505 19980608 20000402 19980623 20050621 20010517 20000426 20010420 20060427 20010612 20000509 20010521 20060502 20030516 20000526 20010607 20060526 20030613 20020630 20030606 20150523 20040507 20080612 20030610 20160417 20050425 20100519 20040512 20160611 20050615 20100614 20070519 − 20070423 20110512 20070609 − 20080412 20110629 20090519 − 20080606 20120419 20130425 − 20080617 20120610 20130429 − 20090523 20120622 20130521 − 20100421 20140508 − − 20100506 20140521 − − 20100509 20150520 − − 20110522 − − − 20120427 − − − 20130515 − − − 20130609 − − − 20140425 − − − 20150515 − − − 20160520 − − − Table 3. The 67 significant EPEs classified by dominant frequency modes for the TMPA 3B42 dataset. The dates refer to the peak precipitation day.
Dominant frequency mode/modes EPE Cases (percentage) Peak precipitation intensity (mm d−1) Synoptic 25 (37%) 33.5 Synoptic&QBW 18 (27%) 36.6 Synoptic&QM 15 (22%) 36.4 Synoptic&ISO 9 (14%) 35.8 No significant bands − − Table 4. The EPE cases (percentage) and the peak precipitation intensity (mm d−1) in each type of dominant frequency mode for the TMPA 3B42 dataset in the pre-summer rainy season during 1998–2018.
Figure 4. Composite of four types of EPEs at the (a) synoptic, (b) synoptic & QBW, (c) synoptic & QM, and (d) synoptic & ISO time scale. P1 and P5 refer to the peak dry and peak wet day on each time scale, and P2 to P4 are the middle phases. The gray bars, black line, red line, orange line and green line refer to the composite raw precipitation (left y-axis), and 3–8-d, 10–20-d, 15–40-d and 20–60-d bandpass-filtered precipitation anomalies (right y-axis), respectively. The x-axis refers to the exact precipitation day relative to the peak wet day (0).
Based on the raw circulation fields, EPEs over SC are all accompanied by intense cyclonic or trough-type systems in the lower troposphere (relative to the climatology), and the heavy precipitation is mainly located at the front of these systems where strong southwesterly moisture flux converges and upward motion takes place (Fig. 5). The BOB, SCS and western Pacific are three major water vapor source regions for EPEs over SC. Specifically, the southwesterly flow anomalies are strongest in the 3–8-d & 20–60-d multiscale combined mode, which transport vast amounts of warm, moist air from the BOB to SC (Fig. 5d). In addition, the northerly anomalies behind the deep mid–high-latitude trough also enhance the intrusion of cold air mass. In the 3–8-d and 3–8-d & 10–20-d modes, the spatial range of the WPSH is more similar to that of the climatology (Figs. 5a and b). A slight eastward retreat and broad westward extension of the WPSH (compared to the respective climatology) happens for the 3–8-d & 15–40-d and 3–8-d & 20–60-d multiscale combined modes, respectively. Despite the slight eastward retreat of the WPSH, a lower-level anomalous high-pressure system exists in the northern Philippines, and the enhanced pressure gradient strengthens the southwesterly flow along the coastal region over SC (Fig. 5c). In comparison, the high-pressure system in the 3–8-d & 20–60-d combined mode is deep and wide, and its western flank even covers the Indochina Peninsula. The strong and stable WPSH ensures a steady stream of moisture supply from the BOB to SC (confirmed by whole column integrated water vapor flux anomalies, not shown). At 200 hPa, the westerly jet core in the 3–8-d & 15–40-d combined mode moves eastwards compared to the climatology (110°E vs 95°E). Because SC is located on the south side of the westerly jet entrance area, it corresponds to a region of upper-level divergence associated with the jet transverse circulation (Ding, 2005), which induces upward motion and therefore strengthens low-level convergence and the subsequent precipitation intensity over SC.
Figure 5. Composite fields of daily precipitation rate anomalies (mm d−1; shaded; relative to climatology) and 850-hPa wind vector anomalies (m s−1), 500-hPa 5880-m geopotential isoline (red solid line) and climatology (red dashed lines), and 200-hPa total wind speed (30 m s−1; orange solid lines) and climatology (pink dashed lines) for the EPEs associated with (a) 3–8-d, (b) 3–8 & 10–20-d, (c) 3–8 & 15–40-d and (d) 3–8 & 20–60-d disturbances. The climatologies here all refer to the composite of specific peak precipitation days. The letters “L” and “H” denote anomalous low- and high-pressure systems.
Clearly, the raw fields in each EPE type exhibit quite diverse circulation characteristics. Hence, the precipitating disturbances at both the high-frequency synoptic scale and the synoptic with low-frequency QBW, QM and ISO combined modes should be respectively analyzed to better understand the formation processes and mechanisms of SC EPEs. In the next section, we focus on exploring different scale disturbances.
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As analyzed in section 3, despite the majority of the 67 identified EPEs being multiscale combined actions, each is accompanied by a typical synoptic-scale mode. Therefore, synoptic-scale disturbances are analyzed first. It has been found that EPE-related systems are most prominent in the lower troposphere, and the influence of synoptic-scale disturbances is relatively small (Liu et al., 2014; Zhang et al., 2017; Huang et al., 2018). Based on MV_EOF analysis, four types of typical synoptic-scale disturbances were identified: mid–high latitude troughs (18 cases; type I), Yangtze–Huaihe cyclones (17 cases; type II), SC cyclones (13 cases; type III), and Yellow–Huaihe anticyclones (8 cases; type IV) (Fig. 6). The precipitating disturbances in the other 11 cases were hard to identify and are therefore not discussed.
Figure 6. Temporal evolution of the 3–8-d bandpass-filtered 850-hPa daily mean wind anomalies (m s−1) and daily precipitation rate anomalies (shaded, mm d−1) for the four types of synoptic-scale disturbances from phase 1 to phase 5. The thick brown curve denotes the trough. The letters “C” and “A” denote cyclonic and anticyclonic circulation anomalies, respectively. Similar conventions apply for the following figures.
In type I, negative precipitation and anticyclonic circulation anomalies control SC in phase 1, with the mid–high-latitude trough appearing north of 35°N (Fig. 6a). Then, the trough deepens and marches rapidly southwards (Fig. 6b). In phase 5, the northerly flows behind the trough guide the cold air southwards to SC, and a large positive precipitation anomaly becomes located at the bottom of the trough (Fig. 6c).
The precipitating cyclonic anomalies in types II and III both originate from downstream of the TP and propagate northeastwards or southeastwards as cyclonic–anticyclonic wave trains, respectively (Figs. 6d–f, Figs. 6g–i). In type II, positive precipitation anomalies occupy the southeast quadrant of the cyclonic anomaly, where the powerful southwesterly flow transports vast amounts of water vapor from the SCS and converges over SC. In contrast, the positive precipitation anomaly always occurs with the cyclonic anomaly from phase 1 in type III. Both cyclonic and precipitation anomalies strengthen as they propagate southeastwards and reach their peak in phase 5. Despite the relatively small spatial scale of the cyclonic anomaly and weak southerly in type III, it is the positive relative vorticity and strong moisture convergence that lead to EPEs in this type.
The last type has the fewest cases, in which the anticyclonic–cyclonic wave train originates from mid–high latitudes and propagates southeastwards. Different from the other three types, it is the northeasterly anomaly with relatively cold, dry air masses that influences SC, and intense positive precipitation anomalies only appear in phase 5, which concentrate along the southeast coastal region. This type of synoptic-scale disturbance is quite similar to that described by Liu et al. (2014), who found that warm, moist background conditions along with cold, dry northeasterly flow can also trigger EPEs. In the multiscale combined action modes, the synoptic-scale disturbances show random distributions of the four types (including atypical cases).
Overall, in pre-summer rainy season, the identified EPE at the synoptic time scale are dominated by the westerly weather systems. The synoptic-scale disturbance classification results in this study are qualitatively consistent with those of Huang et al. (2018), despite the adoption of different precipitation datasets and classification methods (objective vs subjective). It is certain that there are precipitating disturbances from the subtropical ocean area (e.g., SCS) at the synoptic scale, but the number of cases is very limited.
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Low-frequency disturbances are characterized by a longer life history and wider spatial range, which provide more favorable circulation background conditions for EPEs. The subjective classification in this study focuses on the precipitating system on the peak wet day, and then traces backwards to the peak dry day.
Three types of QBW disturbances are distinguished: tropical western Pacific wave trains (type I; 6 cases), extended troughs of the mid–high-latitude cyclonic anomaly (type II; 4 cases), and wave trains downstream of the TP (type III; 4 cases) (Fig. 7). Clearly, the scopes of QBW disturbance activity are wider, ranging from mid–high-latitude to tropical regions, and maintain roughly the same proportion. The anomalous cyclonic–anticyclonic wave train originating from the tropical western Pacific in type I is a typical QBW disturbance that usually affects EPEs or heat waves in eastern China (Liu et al., 2014; Li and Zhou, 2015; Gao et al., 2018). The wave train propagates continuously northwestwards, and the cyclonic anomaly becomes stable over SC in phase 3 before then strengthening with increasing precipitation until reaching its peak in phase 5 (Fig. 7a). In type II, the mid–high-latitude wave train and cyclonic anomaly east of the TP propagate synchronously eastwards and ultimately evolve into the extended trough of the mid–high-latitude cyclonic anomaly on the peak wet day (Fig. 7b). This type of QBW disturbance is quite similar to the low-frequency wave train along the subtropical westerly jet found in Miao et al. (2019). In type III, the coupled cyclonic–anticyclonic anomalies originate from downstream of the TP and then propagate eastwards to the northwestern Pacific (Fig. 7c). The positive precipitation anomaly increases with the strengthening and convergence of the southwesterly flow in front of the cyclonic anomaly from phase 3 to phase 5.
Figure 7. As in Fig. 6 but for the 10–20-d bandpass-filtered disturbances and precipitation anomalies.
The two types of QM disturbances are quite like the 1st- and 3rd-type QBW disturbances, except that the coupled cyclonic–anticyclonic anomalies propagate southeastwards to the SCS in type II (Fig. 8). The intense southwesterly perturbation between the cyclonic–anticyclonic anomaly couplet is the key component for EPEs. Despite the WPSH not showing any westward expansion, an 850-hPa high-pressure system still exists in the raw field on the peak wet day (Fig. 5c). Bandpass filtering reveals that this high-pressure system is the result of the low-level QM-scale anticyclonic anomaly in the northern Philippines, which can be traced to the eastern Philippines or downstream of the TP (Fig. 8). Combined with the findings in Jiang et al. (2020), it can be concluded that the strong southwesterly anomaly is the most critical component for EPEs at the QM scale.
Figure 8. As in Fig. 6 but for the 15–40-d bandpass-filtered disturbances and precipitation anomalies.
At the ISO scale of the 3–8-d & 20–60-d combined mode, there is no direct precipitating disturbance over SC as there is at the other three time scales. The most prominent signals are the westward propagation of the tropical cyclonic and anticyclonic anomalies south of 20°N (Fig. 9). In phase 1, a cyclonic anomaly with two closed centers exists over the SCS and the western Pacific, respectively, and SC is controlled by a northeasterly anomaly with a negative precipitation anomaly. An anticyclonic anomaly simultaneously exists in the tropical Pacific. In phase 2, the cyclonic anomaly with two centers propagates westwards to the eastern BOB and SCS, and the anticyclonic anomaly strengthens and moves westwards. Then, the cyclonic anomaly over the SCS disappears and the anticyclonic anomaly expands further in phase 3. In phases 4 and 5, the cyclonic anomaly propagates to the northern BOB and the anticyclonic anomaly continues to expand and splits into two centers, which are exactly the same as the two centers of the cyclonic anomaly in phase 1. From phase 4, the southwesterly anomaly between the cyclonic–anticyclonic couplet stretches from the BOB to SC, and the abundant water vapor supply and moisture convergence lead to a positive precipitation anomaly over SC. Both the southwesterly anomaly and precipitation over SC reach their peak in phase 5. The bandpass-filtered cyclonic–anticyclonic anomaly couplet is stable and powerful, which is also apparent in the raw field as indicated by the 850-hPa low/high pressure couplet over the BOB and western Pacific (cf. Fig. 9e and Fig. 5d). Although EPEs with the 3–8-d & 20–60-d combined mode can happen in April, May or June, the area of origin and propagation pathway of the bandpass-filtered disturbances are robust, which means the 20–60-d ISO signal may provide early signs for the prediction of heavy precipitation over the SC region.
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Abundant water vapor supply and continuous moisture convergence are the key components for large-scale regional extreme precipitation episodes (Wang et al., 2016; Ding, 2019; Tabari, 2020). In order to reveal the relative contributions/actions of the synoptic-scale and three low-frequency disturbances, the variable most related to precipitation was analyzed. Specifically, the area-averaged 1000–300-hPa vertically integrated moisture flux convergence (VIMFC) over SC was computed and then corresponding bandpass filtering was performed on it to extract the VIMFC disturbance at each time scale. The peak wet day is denoted as day 0, and then variables are traced back to day −3.
For all three multiscale combined modes, the synoptic-scale VIMFC exhibits divergence over SC on day −3 and day −2 (Table 5). From day −1, the synoptic-scale VIMFC becomes convergent and reaches its peak on day 0. On the three low-frequency time scales (QBW, QM and ISO), the VIMFCs are all convergent, with the values gradually increasing and maximizing on day −1 or day 0. In fact, day −3 roughly corresponds to phase 4 of the QBW, QM and ISO modes, which all form a quite favorable cyclonic anomaly or intense southwesterly convergence pattern (Figs. 7–9). Despite both the unfiltered (raw) and low-frequency background circulation fields (> 60 d) being convergent on day −3 and day −2, the synoptic-scale disturbances nevertheless suppress the formation of heavy precipitation. From day −1, synoptic-scale disturbances become phase locked with low-frequency disturbances, and then EPEs occur with the strongest moisture convergence on day 0. In addition, the high-frequency disturbances (< 3 d) all contribute moisture convergence to different degrees on day 0, and the low-frequency background circulation exhibits relatively weak water vapor convergence during day −3 to day 0. It should be mentioned that the amounts of VIMFCs are not proportional to the total EPE intensity because the formation of EPEs is affected not only by the disturbances but also by the topographic conditions and other precipitation generation mechanisms.
Multiscale combined mode Frequency band Day-3 Day-2 Day-1 Day0 Synoptic&QBW HFD* −3.95 −0.19 4.23 −4.42 (4%) Synoptic 26.7 31.68 −15.55 −36.44 (35%) QBW −13.42 −23.86 −30.03 −30.27 (29%) ISO −4.37 −6.94 −9.27 −11.22 (11%) LFBS* −15.08 −15.13 −15.09 −14.95(14%) Unfiltered −8.35 −15.91 −70.62 −104.08 Synoptic&QM HFD −0.59 −2.89 9.98 −15.72 (22%) Synoptic 17.05 15.05 −11.06 −26.81 (38%) QM −22.13 −28.34 −31.5 −31.12 (44%) LFBS −7.02 −7.06 −7.05 −6.98 (10%) Unfiltered −10.37 −15.04 −28.13 −70.09 Synoptic&ISO HFD −6.78 −5.16 15.19 −20.62 (23%) Synoptic 2.06 37.63 −5.52 −28.95 (32%) QBW −2.03 1.04 3.41 4.08 (−5%) ISO −29.57 −32.8 −34.38 −34.17 (38%) LFBS −4.53 −4.41 −4.23 −4.00(5%) Unfiltered −45.17 −9.19 −31.38 −89.26 *HFD and LFBS refer to high-frequency disturbances (< 3 d) and low-frequency background circulation (> 60 d) components, respectively. Values in bold indicate significant positive contribution to EPEs. Table 5. The unfiltered and bandpass-filtered area-averaged 1000–300-hPa VIMFC (vertically integrated moisture flux convergence; 10−6 kg m−2 s−1) over SC for multiscale combined mode.
Because the disturbances are most obvious in the lower troposphere, Table 6 gives the 850-hPa relative vorticity analysis to further illustrate the relative contributions of disturbances on each time scale. The results are similar as those of VIMFCs, i.e., the dominant disturbances in each type of EPEs make a major contribution to the increase of raw positive vorticity on peak wet day.
Multiscale combined mode Frequency band Day-3 Day-2 Day-1 Day0 Synoptic&QBW HFD −0.89 0.96 −0.63 0.04 (0.3%) Synoptic −0.82 −2.87 −0.94 4.99 (43%) QBW −1.29 0.42 2.2 3.58 (31%) ISO −0.44 −0.29 −0.1 0.11 (1%) LFBS 2.39 2.51 2.61 2.69 (23%) Unfiltered −2.01 0.04 2.89 11.55 Synoptic&QM HFD 0.07 −0.11 0.23 −0.49 (−6%) Synoptic 0.31 −3.14 −2.92 3.53 (41%) QM 0.65 1.83 2.86 3.62 (42%) LFBS 1.12 1.16 1.2 1.23 (14%) Unfiltered 2.16 0.32 2.19 8.54 Synoptic&ISO HFD 0.18 −0.08 −0.16 0.52 (4%) Synoptic 1.11 −6.54 −3.62 6.88 (53%) QBW 0.7 0.87 0.8 0.52 (4%) ISO 2.68 3.14 3.45 3.59 (28%) LFBS 0.96 0.96 0.95 0.92 (7%) Unfiltered 5.13 −2.4 1.06 12.98 Note: values in bold indicate significant positive contribution to EPEs. Table 6. As in Table 5, but for the 850-hPa relative vorticity (10−6 s−1).
Datasets | April | May | June |
TMPA 3B42 vs. STN | 0.95 | 0.89 | 0.90 |
CMORPH-CRT vs. STN | 0.80 | 0.71 | 0.64 |
CMORPH-BLD vs. STN | 0.90 | 0.79 | 0.79 |