Comparison of Diurnal Variation of Precipitation System Observed by TRMM PR, TMI and VIRS

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1 Comparison of Diurnal Variation of Precipitation System Observed by TRMM PR, TMI and VIRS Munehisa K. Yamamoto, Fumie A. Furuzawa 2,3 and Kenji Nakamura 3 : Graduate School of Environmental Studies, Nagoya University 2: Japan Science and Technology Agency 3: Hydrospheric Atmospheric Research Center, Nagoya University. Introduction Diurnal variation of precipitation system is one of the interesting topics for meteorology. Infrared data by geostationary satellites are useful to detect the diurnal cycle of cloud activity. However, precipitation activity is not always corresponding to cloud activity. The relationship between the cloud and precipitation is not fully understood. The Tropical Rainfall Measuring Mission (TRMM) satellite bears potential to study the diurnal cycle due to its orbital characteristics by long time accumulation. TRMM has multiple sensors such as Precipitation Radar (PR), TRMM Microwave Imager (TMI) and Visible and Infrared Scanner (VIRS), and TRMM can observe the same precipitation and cloud systems simultaneously. In addition to that, PR estimates not only precipitation amount but also precipitation characteristics (convective and stratiform) and vertical structure. In this study, we compared diurnal variations observed by the TRMM onboard sensors. The regional characteristics of diurnal variation is also examined. 2. Data This study used TRMM version 5 standard products in 998 3, June July August (JJA). Variables are summarized in Table. Datasets for PR, TMI and VIRS are processed in the following order. At first, the raw data are averaged into.25 by.25 grid boxes. VIRS and TMI data are adjusted to PR swath to decrease the sampling effect. Next, the averaged data are accumulated every hour local time. And the peak local time of maximum rainfall (minimum brightness temperature) for PR and TMI (VIRS) are detected. In addition to these data, 3B42 version 6 surface rain data are also utilized as the reference of less sampling error. * Corresponding author address: Munehisa K. Yamamoto, Hydrospheric Atmospheric Research Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, , Japan. TEL/FAX: yamamoto@satellite.hyarc.nagoya-u.ac.jp 3. Results 3. Diurnal variation of precipitation revealed by 3B42 Figure a shows the peak local time of maximum rain rate derived from 3B42 averaged for 6 years in JJA. Generally, the peak local time corresponds to previous studies. For example, over land, afternoon peak rainfall appears on the plain such as western and southeastern United States, Ganges River Basin and the Yangzi River Basin, and also on the Tibetan Plateau. On the contrary, nighttime peak rainfall appears on the Rocky Mountain, the Deccan Plateau, and so on. A few hours time shift of the peak time are also illustrated. While the time shift from the late night to early morning appears over land such as central United States, coastal Amazon, southern slopes of Himalaya, the time shift from the early morning to early afternoon appears over ocean near the coast such as Gulf of Mexico, west coast of United States, maritime continent and Bay of Bengal. The strength of diurnal variation is shown in Fig. b. As an index of the strength of diurnal variation, Normalized Differential Rainfall Index (NDRI) is defined by RRmax RR NDRI = () RRmax + RR where RRmax is the rain rate at peak local time and RR is the averaged rain rate. The larger NDRI appears, the stronger the diurnal variation is. While large NDRI (>.3) are mainly represented over the land region, for example, middle and west United States, Tibetan Plateau, maritime continent, some island and the coastal region of Amazon, small NDRI are represented over the oceanic region such as the Intertropical Convergence Zone (ITCZ) and northwestern Pacific. However, large NDRI appears even for oceanic region such as Gulf of Mexico, maritime continent, and western Bay of Bengal. 3.2 Comparison of peak local time among the sensors The spatial distributions of peak local time derived from PR, TMI and VIRS are shown in Fig. 2a, b, c, respectively. Compared with the distribution of peak local time derived from 3B42 (Fig. a), that derived from PR, TMI and VIRS are generally similar especially for the region where the large NDRI is illustrated (Fig. b), although the variations of peak

2 local time are large. The peak time shifts also illustrate for PR, TMI and VIRS. However, the shifts are indistinct compared with 3B42. For VIRS, nighttime peak appears over north and south Africa, Australia and eastern Pacific and Atlantic Ocean in the midlatitude. These regions correspond to dry region, so the nighttime peak show the diurnal variation of surface temperature. Hereafter, we pay attention to the regions where NDRI is large. Making comparison with PR, TMI and VIRS, systematic time shifts appeared. For example, over the western and eastern United States, India and Tibetan Plateau, 2 6 LT peak dominants in PR. For TMI and VIRS, 4 8 LT peak become dominant. Over the oceanic region such as Gulf of Mexico and west Bay of Bengal, 2 6 LT peak is more distinct over offshore in VIRS. Figure 3 shows the histograms of the peak time difference among the sensors, that is TMI PR, VIRS PR and VIRS TMI, over the global ocean, global land and some land regions. As a common feature, no time shift dominants over all of the regions. Focusing on broad component, many distributions are shifted to the right over the land region, especially for North America. In other words, the peak local time for PR is earlier than that for TMI and the peak local time for PR and TMI is earlier than that for VIRS. The shift is more distinct for VIRS PR and VIRS TMI. 3.3 Causes of time shifts among the sensors Figure 4 shows the fraction of convective rain frequency to the total rain derived from PR 2A25. Convective rain dominated regions appear in such as the United States, Amazon and the maritime continent, Gulf of Mexico and the western Bay of Bengal. These regions correspond to large NDRI region. However, over the Tibetan Plateau, the convective rain fraction is small in spite of large NDRI. Basically, the rain is classified as convective in the case of large reflective factor (>39 dbz) (Awaka 998). Convective but relatively weak and shallow precipitation may dominant so that the convective rain fraction is small. Above results suggest us to hypothesize that the sensors signatures at each stage of convective rain systems cause the time shift. A schematic diagram of the development of convective rain system is illustrated in Fig. 5. PR directly detects near surface rain at the initial to mature stage. TMI mainly observed the deep convection and retrieve strong rain with solid hydrometeor in the mature stage. VIRS detects the deep convective clouds and the anvil from mature to decaying stages. PR and TMI observe the rain and hydrometeor directly, and then the peak time difference is small. VIRS observes the cloud top height so that the peak time difference is large. In order to remove the cirrus-type clouds such as anvils, a split window technique is introduced (Inoue ). For VIRS, brightness temperature difference (BTD) is defined as BT(ch. 4) BT(ch. 5). We selected some thresholds of BTD:.5, 2.5 and 3.5 K. If the BTD is more than these thresholds, the pixel was eliminated. In other words, cirrus-type clouds are strongly removed by small BTD thresholds (not shown). We find that morning peak become much clear especially for the United States, western India and northwestern Atlantic. While the relatively weak convective rain dominated region such as the east part of India, Middle Africa shows small time shift. These features support to our hypothesis. 4. Summary and Conclusion This study examined characteristics of diurnal variation using TRMM for 6 years orbit. The characteristics of diurnal variation from combined product and each sensor is consistent with previous studies. Making comparison among the sensors, the systematic peak time shift is illustrated. The order is PR first, followed by TMI and VIRS by a few hours. These regions correspond to the regions where the strong diurnal variation appears. The time shift may be caused by the sensors characteristics in the development of convective precipitation and cloud system. References Awaka, J., 998: Algorithm 2A23 rain type classification. IHAS and ESTO, Eds., Proc. Symp. on the Precipitation Observation from Non-Sun Synchronus Orbit, 25 2, Nagoya, Japan. Inoue, T., : Early results on comparison between cloud information by VIRS and rain information by PR/TMI. Adv. Space Res., 25,

8 22 24 (b) NDRI (RR(maxLT)-RR(ave))/(RR(maxLT)+RR(ave)) 98-3 JJA 3B42 - - 6 8 2 3 36 [NDRI]..5..25.3.35..45.5.55.6.65 Figure.

3 Table. Datasets for this study. Sensor Product Ver. Description VIRS B 5 IR Brightness Temperature ch. 4 (.8 µm), ch. 5 (2. µm) TMI 2A2 5 Surface rain rate PR 2A25 5 Near surface rain rate COMB 3B42 6 Surface rain rate (a) LT(maxRR) 98-3 JJA 3B [LT] (b) NDRI (RR(maxLT)-RR(ave))/(RR(maxLT)+RR(ave)) 98-3 JJA 3B [NDRI] Figure. (a) Peak local time of maximum precipitation and (b) NDRI (definition is in text) derived from 3B42 for in JJA. For (b), pixels with few rain amount where the averaged rain was less than mm month - are blanked out.

(a) LT(maxRR) 98-3 JJA PR2A25 - - 6 8 2 3 36 (b)

98-3 JJA VIRSB - - 6 8 2 3 36 [LT] 2 4 6 8 2 4 6 8 22 24

4 (a) LT(maxRR) 98-3 JJA PR2A (b) LT(maxRR) 98-3 JJA TMI2A (c) LT(minBT) 98-3 JJA VIRSB [LT] Figure 2. Peak local time of maximum precipitation derived from (a) PR and (b) TMI and (c) peak local time of minimum brightness temperature derived from VIRS for in JJA.

(b) Global Land (c) Asia (d) North America VIRS-PR TMI-PR VIRS-TMI -2 - -8-6 -4-2 2 4 6 8 2-2 - -8-6 -4-2 2 4 6 8 2-2 - -8-6 -4-2 2 4 6 8 2 (a) Global Ocean (e) South America - (c) (d) (e) - 6 8 2

5 (b) Global Land (c) Asia (d) North America VIRS-PR TMI-PR VIRS-TMI (a) Global Ocean (e) South America - (c) (d) (e) Figure 3. Histograms of peak local time difference over (a) the global ocean, (b) the global land, land regions of (c) Asia, (d) North America and (e) South America for TMI-PR (solid line), VIRS-PR (dotted line), and VIRS-TMI (dash-dotted line). Conv. fraq. (freq.(conv)/freq.(all)) 98-3 JJA PR2A [ratio] Figure 4. Fraction of convective rain frequency to the total rain derived from PR2A25 for in JJA. Initial Mature Decaying VIRS TMI PR Time Figure 5. Schematic diagram of the relationship between the sensors signature and development of convective precipitation system. Clouds show the stages of development. Blue line and red line show the rain rate for each stage for PR and TMI, respectively. Black line is same as PR and TMI but for brightness temperature.

Comparison of Diurnal Variations in Precipitation Systems Observed by TRMM PR, TMI, and VIRS

15 AUGUST 2008 Y A M A M O T O E T A L. 4011 Comparison of Diurnal Variations in Precipitation Systems Observed by TRMM PR, TMI, and VIRS MUNEHISA K. YAMAMOTO Center for Environmental Remote Sensing, Chiba