Microphysical Properties of Precipitation over Complex Terrain inferred from TRMM, GPM, and IPHEX Observations S. Joseph Munchak 1,2 *, Shoichi Shige 3 1: NASA Goddard Space Flight Center, Greenbelt, MD, USA; 2: University of Maryland, College Park, MD, USA; 3: Division of Earth and Planetary Sciences, Kyoto University Nature of Orographic Precipitation In-Situ Observations from IPHEX Case Studies using a TRMM Combined Algorithm Statistics from GPM DPR and Combined Algorithms According to Houze 1993 (Cloud Dynamics), terrain can influence the formation of precipitation in the following ways: a) Seeder- feeder mechanism; b) forced ascent; c) upslope convection initiation; d) upstream convection initiation; e) thermal convection initiation; f) lee-side convection initiation; and g) lee- side convection enhancement. Because profiles of vertical velocity and cloud liquid water can be quite different from non-orographic precipitation, passive microwave algorithms which rely on ice scattering relationships to surface precipitation may be biased in these regimes (Shige et al., 2013). The primary factors which influence the characteristics of precipitation in the vicinity of terrain are: Large-scale wind field Terrain height, slope, and shape (isolated peak vs. continuous ridge) Thermodynamic fields (water vapor and temperature profiles) The Froude number (Fr=u/NΔz) represents the ratio between horizontal velocity (u) and energy required to lift a parcel of air over a mountain (which depends on the height of the mountain, Δz, and the static stability represented by the buoyancy frequency N). Fr determines the response of the flow to an obstacle. If Fr = 1, flow will ascend the mountain or ridge resulting in scenarios b or c. The large-scale environment also matters. Synoptic lift at high altitudes combines with boundary-layer forced ascent in the seeder-feeder scenario (a). Wind shear and instability influence the convection that is triggered by terrain (c-g). In summary, the response of precipitation to terrain is complicated and the mechanism must be considered in analysis of microphysics and algorithm improvements. DateEvent Type925mb wind 850mb wind mb Fr 2 TPW (mm) 9 May 2014Forced ascent transitioning to squall line (frontally triggered); possible seeder-feeder 10 May 2014Upslope convection initiation; seeder-feeder stratiform 15 May 2014Widespread frontal stratiform (seeder-feeder) 18 May 2014Widespread stratiform (no orographic enhancement) 25 May 2014Upstream and thermal convection initiation 26 May 2014Thermal convection 29 May 2014Thermal convection 8 June 2014Lee-side convection enhancement 10 June 2014Squall line (possible seeder- feeder) 11 June 2014Upslope convection 12 June 2014Thermal convection Disdrometer locations during IPHEX (square=2DVD, cloud=APU). Map covers 34-36N and 81-85W. Above: Topography. Below: 24-hour rainfall total on 15 May The May 15 case presented the best widespread precipitation event with large- scale forcing during IPHEX. Radar-based precipitation estimates from the MRMS system show heaviest amounts just along and south of the southern-facing slopes of the Appalachians, where maximum orographic enhancement would be expected. Four APU disdrometers were located roughly in line with the wind vector and provide an opportunity to examine the effect of orographic ascent and descent on the DSD. APU09 77 mm APU01 62 mm APU02 43 mm APU06 41 mm APU09 APU01 APU02 APU06 The rain parameter diagram (bottom right) illustrates the DSD characteristics at the four sites. At low reflectivity, the upslope sites, APU09 (77 mm total) and APU01 (62 mm), had DSDs with smaller mass-weighted drop diameter and higher water content than the downslope sites APU02 (43 mm) and APU06 (41 mm). These tendencies reversed at higher reflectivity (40 dBZ and above), but fewer samples were available at these intensities. This indicates that most of the seeder-feeder orographic enhancement occurred during times of lighter rainfall, while microphysics were not as sensitive to terrain forcing during deeper convection. Histograms of rainfall by reflectivity also show a reduction in the contribution of low-reflectivity DSDs to total rainfall at the downslope sites. Six months of GPM DPR data (March- September 2014) were examined for orographic precipitation. Profiles were selected if the orographic-induced vertical motion ( ) was greater than 0.1 m/s in magnitude. This map displays the location of these profiles (green=rain, blue=snow). The impact of vertical motion on profile characteristics and GMI brightness temperatures is examined below. Both the DPR MS (Ku+Ka) algorithm and the DPR+GMI CORRA MS (Ku+Ka_) algorithm indicate increased occurrence of > 1 mm/hr precipitation rates and reduced occurrence of light ( 0 and/or overestimate when w < 0. CFAD diagrams of KuPR reflectivity indicate that profiles with positive vertical motion have higher reflectivity than those with downward vertical motion, especially in the 2.5 km above the surface (snow profiles) or freezing level (rain profiles). The DFR (Ku-Ka difference) CFADs are also higher in snow near the surface, and near the melting level in rain profiles. Both higher reflectivity and higher DFR indicate larger precipitation particles in the profiles with upward motion compared to those with orographically forced downward motion. This case (TRMM orbit #54787 on 28 June 2007) consists of thermal and upslope-triggered convection over the Sierra Madre mountain range in Mexico. This intense convection had echo tops near 15km and reflectivity cores > 40 dBZ up to nearly 10km. Some scattering is evident at 19 GHz indicating large graupel and/or hail. The TRMM combined PR+TMI algorithm retrieved abnormally large ice particles in the convective core with smaller particles in the trailing stratiform region. Abnormally large raindrops were also retrieved as would be expected with the melting of graupel and hail, and this tendency extended into the stratiform region. This case (TRMM orbit #54821 on 30 June 2007) is an example of the upstream blocking scenario leading to convection initiation to the west of the Ghats mountains in India. The reflectivity, vertical extent, and scattering signatures are weaker than in the Sierra Madre case, but rainfall rates are still intense as the convective cores have much smaller drops due to a more active warm rain process. The combined algorithm also indicates a preference for large aggregates (not graupel) in this system. Further research will elucidate how much of these differences are a result of the environment versus the nature of the orographic forcing. Controlling for precipitation intensity at the surface reveals more distinctions between the upward and downward-forced profiles. In light snow (<2 mm/hr), the upslope profiles tend to have lower reflectivity from 1-3km above the surface than downslope profiles without a change in DFR, indicating less vertical extent. This tendency is reversed at heavier rates. In rain, upslope profiles have lower reflectivity both above and below the bright band, but a higher DFR. This indicates reduced vertical extent but larger particles above the melting layer, and more attenuation/smaller raindrops in the rain layer than the downslope profiles of the same rain rate. The above plots show the difference in GMI brightness temperature at 89, 166, and 183 GHz between the upslope and downslope profiles after controlling for surface precipitation rate (provided by the DPR MS algorithm) and column water vapor. In nearly all cases, the upslope profiles have warmer Tbs than the downslope profiles at the same precipitation rate, consistent with previous work by Shige et al. (2013, JAMC), and consistent with the DPR profile analysis above that shows weaker vertical extent for the upslope profiles.