Assessment of vertical air motion among reanalyses and qualitative comparison with direct 1 VHF radar measurements over the two tropical stations 2

10 Vertical wind (w) is one of the most important meteorological parameters for 11 understanding different atmospheric phenomena. Only very few direct measurements of w are 12 available and most of the time one must depend on reanalysis products. In the present study, 13 assessment of w among selected reanalyses, (ERA-Interim, ERA-5, MERRA-2, NCEP-2 and 14 JRA-55) and qualitative comparison of those datasets with direct VHF radar measurements over 15 the convectively active regions Gadanki (13.5 o N and 79.2 o E) and Kototabang (0 o S and 100.2 o E) 16 are presented for the first time. The magnitude of w derived from reanalyses is 10-50% less than 17 that from the direct radar observations. Radar measurements of w show downdrafts below 8 to 10 18 km and updrafts above 8-10 km over both locations. Inter-comparison between the reanalyses 19 shows that ERAi is overestimating NCEP-2 and underestimating all the reanalyses. Directional 20 tendency shows that the percentage of updrafts captured is reasonably good, but downdrafts are 21 not well captured by all reanalyses. Thus, caution is advised when using vertical velocities from 22


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Vertical air motion (w) in any region of the Earth's atmosphere reflects the structure and 27 dynamical features of that region. Importantly, in the lower part of the atmosphere, sudden 28 widespread changes in weather are usually associated with variations in the vertical air motion.
climatological mean vertical velocity (e.g. Uma and Rao, 2009b). The EAR provides quality 119 check data online (http://www.rish.kyoto-u.ac.jp/ear/data/index.html). The EAR operates 120 continuously and this study uses every hour data (diurnal data of single day) from 2001 to 2015. 121 The EAR data during convective periods are eliminated following the same criteria as for the 122 IMSTR, a second screening step. Each full diurnal cycle (after removing convective profiles) is 123 averaged and considered as a single daily profile for the EAR. For both radars, vertical velocity 124 (in cm s -1 ) is directly estimated using equation (1) 125 (1) 126 where  is the radar wavelength (in cm) and f d is the Doppler velocity (Hz). 127 It is known that estimates of w derived from VHF radar measurements are vulnerable to 128 biases due to tilting layers, strong horizontal winds (e.g., jet-stream), complex topography, 129 Kelvin-Helmholtz instabilities and gravity waves (Rao et al., 2008 and references therein). Rao 130 et al., (2008) has discussed in detail the biases that can cause spurious diagnosis of downward 131 wind as proposed by Nastrom & VanZandt (1994). In addition, they have also discussed the 132 potential biases caused by beam pointing errors as mentioned by Hauman and Balsley (1996) and 133 have conducted critical analysis to rule out beam pointing biases from VHF radar data. As 134 proposed by Nastrom & VanZandt (1994) on the bias caused by gravity waves, Rao et al., (2008) 135 have investigated biases caused by gravity waves by calculating the variances and found that 136 downward wind below 10 km are not affected by gravity waves. Their analysis clearly showed 137 that the mean downward motion below 10 km and upward motion above 10 km are real and not 138 caused by measurement biases, and also that the existing biases do not change the direction of 139 the background w when measurements are averaged over longer periods. 141 We use 6-hourly vertical velocities from the European Centre for Medium-Range 142 Weather Forecasts (ECMWF) Interim reanalysis (ERAi) from 1995 to 2015(Dee et al., 2011.

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The nearest grid points are taken for Gadanki (13.68  When compared to ERAi, the fifth ECMWF reanalysis (ERA5) provides much higher 148 spatial (30 km) and temporal resolution (hourly) from the surface up to 80 km (137 levels).

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ERA5 also features much improved representation especially over the tropical regions of the  For all the reanalyses data, w (in cm s -1 ) is estimated using the formula: where  is the vertical velocity in pressure coordinates (in Pa s -1 ), T is the absolute temperature 185 (K), p is the atmospheric pressure (hPa) and R (=287 J kg -1 K -1 ) is the gas constant. To compare measured vertical wind with the reanalysis products, we take the reanalysis data corresponding to 187 12 GMT for Gadanki and the daily mean for Kototabang. Updrafts are observed in the TTL from September to November but the peak in the updrafts is 210 shifted lower than that observed by the IMSTR. Below 8 km, IMSTR shows downdrafts from 211 April to October. It is notable that the reanalyses only produce downdrafts below 2 km and are 212 unable to reproduce the downdrafts above 2 km. Earlier studies using the IMSTR showed similar 213 seasonal characteristics for w (Rao et al., 2008). Gadanki (although no such systematic differences is observed in Kototabang). The directional tendencies are also similar in both the profiles at both locations. This analysis shows that the higher frequency compared to the updrafts (<10 %). However, these ratios decrease above 10 301 km. By contrast, the percentage of downdrafts produced during JJA and SON is less than that of 302 the updrafts, with frequencies less than 25 % in all the levels during these seasons. The

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Our analysis reveals that downdrafts are not well produced in reanalyses, and also the location of 336 the largest updrafts is shifted lower than in the observations. Hence the reanalyses should be used 337 with care for representing various atmospheric motion calculations (viz. diabatic heating, 338 convection, etc.,) that mainly depend on the direction of w. This study provides the reanalysis 339 community an initial basis to improve the methodology for calculating w in reanalyses, as this is 340 a much sought-parameter for atmospheric circulation calculations and analyses.

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Gadanki data are at 12 GMT and Kototabang data are diurnal mean.           Figure 8. Comparison of relative differences in vertical velocity (w) between the reanalysis for Gadanki (solid line) and Kototabang (dash line). Individual month differences are estimated relative to ERAi and then averaged for each month.