Presentation on theme: "THE REGIONAL AND DIURNAL VARIABILITY OF THE VERTICAL STRUCTURE OF PRECIPITATION SYSTEMS IN AFRICA, BASED ON TRMM PRECIPITATION RADAR DATA ** Teferi Dejene."— Presentation transcript:
THE REGIONAL AND DIURNAL VARIABILITY OF THE VERTICAL STRUCTURE OF PRECIPITATION SYSTEMS IN AFRICA, BASED ON TRMM PRECIPITATION RADAR DATA ** Teferi Dejene and B. Geerts* Department of Atmospheric Science, University of Wyoming * Corresponding author address: Dr. Bart Geerts, Department of Atmospheric Science, University of Wyoming, Laramie WY 82071, USA; ** based on a paper with the same title, submitted to J. Climate, currently in review. Monday Session 8 th International Conference on Precipitation, Vancouver BC 8-11 August Introduction References Acknowledgements: this study is supported by NASA EPSCoR grant The first author also has the benefit of a NASA Goddard Summer Graduate Student Fellowship, during June-Aug An understanding of the vertical structure of precipitating systems is important, especially in tropics, not just because it implies differences in Z-R relationships and surface rainfall, but also because it affects the vertical structure of latent heating, and therefore has implications for the global atmospheric circulation. Very few studies have been conducted on the characterstics of precipitation over the African region using radar data, and the few published case studies (e.g. Roux et al 1984) do not characterize the population. It is unlikely that a ground-based network of radars will cover Africa anytime soon. Our work is especially relevant for AMMA, as it places the regional AMMA observations in the context of typical patterns across Africa. Spaceborne sensing, therefore, is the most reliable and detailed resource for precipitation studies in the African region (Adeyewa and Nakamura 2003). Yet IR, passive microwave, and radar-based estimations of surface rainfall satellites all have limitations. We aim to characterize the vertical structure of precipitation systems in different regions of Africa, and the diurnal and seasonal variations, by means of reflectivity profiles collected over 5 years by the TRMM Precipitation Radar (PR). TRMM-based studies have shown that the Congo Basin, as compared to the Amazon Basin, has deeper storms, more lightning activity, higher peak reflectivity, and a stronger 85 GHz ice scattering signature (Boccippio et al 2000, Peterson and Rutledge 2001, Toracinta et al 2002). In all of tropical Africa, including the Tropical Wet region (which is mainly the Congo basin), storms tend to be more vigorous than over the Amazon. Storms over the Amazon tend to be more shallow, and warm-rain events (whose echoes peak below the freezing level) are more common. Amazon storms are less likely to have high reflectivity values aloft, and they have a better-defined bright-band signature. Some regional differences in storm vertical structure exists within tropical Africa, although these are small compared to that between the Amazon and Africa. Vigorous storms frequent the Sahel, and to a lesser degree the adjacent northern Savanna and the Tropical Wet region (in the latter especially in DJF). Sahel storms are marked by high echo tops and high hydrometeor loading aloft. The lack of a clear bright band spike suggests that these storms are mostly convective. Comparing the northern (semi-)arid regions (the Sahel and the Sahara) to the southern ones (the Okavango and Kalahari, respectively), low-level evaporation is more common/intense in the former regions and warm rain events are more common in the latter. The diurnal modulation of rainfall and vertical storm structure is minimal over the Amazon and largest in the arid regions of Africa. In many African regions the echo tops are highest and rainfall is most intense in the afternoon (15-18 LT), but the diurnal peak is delayed by a few hours in the Sahel and the northern Savanna. The echo top height and rainfall distribution in the Tropical Wet region has a secondary maximum in the second half of the night. The regional variation of the vertical storm structure in Africa, and the Africa - Amazon contrast, are consistent with thermodynamic properties of the basic-state environment. 2.Data source ItemSpecification Frequency GHZ Swath width220 km Minimum detectable signal dBZ Horizontal resolution4.5 km at nadir Vertical resolution250 m at nadir Antenna beam width Antenna scan angle 0.71° x 0.71° ±17°(cross track scan) Table 1. TRMM PR Specs Fig 1. TRMM Precipitation Radar sampling strategy Data used in this study: 2A25 volumetric PR reflectivities and rainfall rates. The 2A25 reflectivity profiles are corrected for attenuation by heavy rain and the rainrates are corrected for non- uniform beam filling (NUBF). Five years of TRMM 2A25 reflectivity profiles and surface rain rate are used for two seasons, DJF and JJA, over Africa ( ). Diurnal variability is studied by binning the data in 3 hour intervals (local time, not UTC). This is rather course, but the sample size becomes too small at higher temporal resolution. The African region is divided into 9 climatologically homogenous regions (see Fig 3). For comparison, we also include the central Amazon Basin. 3.Analysis method. 4.Reflectivity profiles: frequency by altitude diagrams stratiform Sahara stratiform Congo The frequency by altitude diagram (FAD, Fig 4) shows the normalized probability of encountering a certain reflectivity value at a certain height. The normalization count is based on all levels for all profiles with rain at the ground. Weak-rain profiles ( 8 mm/hr), they have a lower median reflectivity and a lower echo top (Fig 4). The stratiform profiles in the Congo region appear more robustly stratiform than in the Southern Savanna, where the stratiform profiles often appear as light-rain convective residue with a high cloud top and no bright band. The far left sequence shows a remarkable transition from the Sahara, where storms are deep and much evaporation occurs below the bright band, to the Horn or East Africa where storms are more shallow and more warm rain events appear to occur. Virga profiles (those with measurable reflectivity aloft and no 2A25 surface rain) are far more prevalent in the Sahara (and Sahel) then elsewhere (Table 2). Warm rain profiles (those with the highest level with a detectable echo no higher than 4 km) are far more common in the Amazon than anywhere else in Africa (Fig 6, Table 2). Fig 4. Probability density functions of reflectivity-by-altitude for all cases with PR-detected surface rain rate (RR) 0 8 mm hr -1 (right column), for all regions with a JJA wet season, based on JJA A25 data. The two right columns are the same, but for the southern regions with a DJF rainy season. The probability is normalized, i.e. it is the number of occurrences per 2 dBZ per 250 m, divided by all occurrences in all reflectivity and height bins, and expressed as a percentage. The total number of occurrences is shown in the upper right corner of each plot, in thousands. Vertical lines are drawn at 30 and 40 dBZ, and horizontal lines at 4, 7 and 10 km height. 7.Diurnal variability Fig 2. TRMM data flowchart (from Fig 3. Definition of regions used in this study. The seasonal march of rainfall in Africa (5 years of 3B-42 data) is shown as well. Fig 7. Probability density functions of 20 dBZ echo tops, for all surface rain profiles. The probability is normalized, i.e. it is the number of occurrences per 250 m, divided by all occurrences in all height bins, and expressed as a percentage. PDFs shown in Toracinta et al. (2002) and Short and Nakamura (2000) are shown as well. The latter source has a PDF for JJA and one for DJF. The Toracinta et al. (2002) PDF is for large systems only (at least 4 adjacent PR pixels) with a 85 GHz brightness temperature of 250 K or less. * Deep systems are relatively more common over the Sahel and the Sahara. Also over the Congo basin in DJF. * Shallow systems are most common over the Amazon * For many rather weak systems, containing ice (cold clouds), TRMM only detects the bright band, not the cloud tops. The evaporative index (EI) is defined as the reflectivity at 4.0 km minus that at 2.0 km height. The stratiform index (SI) is defined as the reflectivity at 7.0 km minus that at 4.5 km height. The hydrometeor precipitable water (HPW) is defined as the vertically integrated liquid or frozen hydrometeor content, and is expressed as a depth of water (mm) The storm productivity index SPI is defined as the ratio of the surface rainrate R (mm hr -1 ) over the vertically integrated hydrometeor content HPW Fig 8. Diurnal variation of TRMM-PR based variables for (a) JJA regions; (b) mostly DJF regions. 9.Conclusions Adeyewa, Z.D., and K. Nakamura, 2003: Validation of TRMM Radar Rainfall Data over major climatic regions in Africa. J. Appl. Meteor., 42, Boccippio, D. J., S. J. Goodman, and S. Heckman, 2000: Regional differences in tropical lightning distributions. J. Appl. Meteor., 39, 2231–2248. Lebel, T., and A. Amani, 1999: Rainfall estimation in the Sahel: What is the ground truth? J. Appl. Meteor., 38, 555–568. Negri, A. J., T.L. Bell, and L. Xu, 2002: Sampling of the Diurnal Cycle of Precipitation Using TRMM. J. Atmos. Ocean. Tech, 19, 1333–1344. Petersen, W. A., and S. A. Rutledge, 2001: Regional Variability in Tropical convection: Observations from TRMM. J. Climate, 14, Roux, F., Testud, J., Payen, M., Pinty, B., 1984: West African squall-line thermodynamic structure retrieved from dual-Doppler radar observations. J. Atmos. Sci., 41, 3104–3121 Toracinta, E.R., D.J. Cecil, E. J. Zipser, and S.W. Nesbitt, 2002: Radar, passive microwave, and lightning characteristics of precipitating systems in the Tropics. Mon. Wea. Rev., 130, * Only the near-zenith beams (±5°) are used in this study Table 2. Region - season virgawarm rain Sahara - JJA282.9 Sahel - JJA163.1 Savanna North - JJA155.7 Tropical Wet - JJA129.7 East Africa - JJA129.0 Horn Africa - JJA133.1 Kalahari - DJF156.4 Okavango - DJF136.7 Savanna South - DJF127.2 Tropical Wet - DJF157.4 East Africa - MAM108.0 Amazon - JFM Table 3 Region - season rainrateEISIHPWSPI mm/hrdBZ mmhr -1 Sahara - JJA Sahel - JJA Savanna North - JJA Tropical Wet - JJA East Africa - JJA Horn Africa - JJA Kalahari - DJF Okavango - DJF Savanna South - DJF Tropical Wet - DJF East Africa - MAM Amazon - JFM Fig 5. As Fig 4, but for virga profiles only, in the Sahara and the Tropical Wet (JJA) region. Note that the maximum reflectivity plotted is only 30 dBZ. The last color belt peaks at 32 dBZ in the Sahara and 33 dBZ in the Tropical Wet region. Fig 6. As Fig 4, but for for warm-rain profiles only, in the Tropical Wet (JJA) region. 5. Some indices characterizing the precipitation profile6. Regional variations in echo top height average surface rain rate surface rain total average 20 dBZ echo top height Fig. 9 Diurnal variation of the reflectivity FAD for the Sahel [units (2 dBZ) -1 (250 m) -1 ]. The normalized frequencies are expressed as a difference from the normalized 24-hour mean values. The total number of occurrences in a time bin is shown for each FAD Fig 10. Frequency distribution of reflectivity values (over 17 dBZ) with height. Three 6-hour periods are isolated: 3-9 LT (left); 9-15 LT (middle); and LT (right). Frequencies are normalized by the total number of rain profiles in each region. The top panels apply for the JJA regions, the bottom ones for the DJF regions. Note that all x axes are the same. 8.Regional variation of low-level moisture and moist static energy Fig 11. The climatological surface relative humidity, according to the NCAR/NCEP reanalysis dataset, for JJA (north of the equator) and DJF (south of the equator). Fig 12. Vertical profiles of θ*e (curved lines) plotted as a function of pressure (log coordinates, i.e. height) for various regions. The vertical lines are corresponding values of surface θe, derived from the temperature and the specific humidity at 1000 mb. Data are based on the NCAR/NCEP reanalysis dataset. These data are averaged over the full area of each region except for the Sahara, where only the southern third (15-20ºN) is used. regionseasonCAPE e (J kg -1 )LFC e (mb)LNB e (mb) SahelJJA7, SaharaJJA2, Tropical WetDJF6, Tropical WetJJA5, AmazonJFM3, Table 4. Values of CAPEe, LFCe, and LNBe inferred from the profiles shown in Fig 12.