Satellite Meteorology Laboratory (METSAT) 위성관측에서 본 한반도 강수 메카니즘의 특성 서울대학교 지구환경과학부 손병주, 유근혁, 송환진

Rainfall comparison (US) Radar Gauge [Tian et al., 2007, JH]

[Sohn et al., 2010, JAMC] JJA Rainfall comparison (Korea)

JJA

Rain measurement from space (MW) LAND ε s ~ 0.9 Ocean TB(85 GHz) ε s ~ 0.45 TB(19, 22 GHz) ε = 1 ColdWarm Scattering

Examples (MW images)

Flight Speed: 7.3 km/sec PR: Precipitation Radar TMI: TRMM Microwave Imager VIRS: Visible/IR Scanner TRMM

TMI TB85V vs. rain rate 2 TB85 vs. AWS rain rateTB85V vs. GPROF-V6 rain rate [Ryu et al. (2011) JAMC, Revised]

AWS 강우자료 및 위성관측 강우의 일변화 연구 TRMM PR reflectivity, CTH, rain rate TRMM TMI 85V GHz TB Collocated data CTH:cloud top height, TB85V vertical dist. reflectivity, RR Comparison to US Korea US (Oklahoma) JJA

PDF of CTH and TB85V Korea US PR cloud top height TMI TB85V

CFADs of PR reflectivity classified by rain rate 0 < RR PR < 1010 < RR PR < 20 Korea US 20 < RR PR < 4040 < RR PR CFADs: Contoured frequency by altitude diagrams (Yuter and Houze, 1995)

PDF classified by PR rain rate Cloud height (TRMM PR ) TB85V (TRMMTMI)

Analysis region - Korea and Central US (Oklahoma) TRMM TMI, PR - JJA 2003~2006 Aqua MODIS, CloudSat, TRMM TMPA, AWS, ERA-interim - JJA 2007~ DATA Rain features Weather state analysis (K-Means clustering) TMPA, AWS Rainfall Aqua MODIS COT and CTP CloudSat profiles Aqua MODIS COT and CTP Joint histogram (7Х7) ERA-interim Synoptic fields Synoptic patterns TRMM TMI 85V GHz TB TRMM PR Reflectivity, CTH, Rain rate Frequency distributions Daily, 1º×1º Cloud structure CloudSat profiles

A-Train constellation (CloudSat, Aqua)

[Platnick, et al., 2003] MODIS Cloud Products CTP COT

CloudSat Cloud Profiling Radar: 94GHz

Tropical Strom Andrea on Wednesday, 9 May 2007 Cyclone Measurements from CPR

Radar Backscatter Profiles 2B-GEOPROF Cloud Geometrical Profile 2B-CLDCLASS Cloud Classification 2B-LWC-RO Radar-only Cloud Liquid Water Content 2B-IWC-RO Radar-only Cloud Ice Water Content 2B-TAU-OFF-N Cloud Optical Depth – Off Nadir 2B-LWC-RVOD Cloud Liquid Water Content 2B-IWC-RVOD Cloud Ice Water Content 2B-GEOPROF-LIDAR Cloud Geometrical Profile from CPR & CALIOP 1B-CPR 2B-FLXHR Fluxes and Heating Rates 2B-CLDCLASS-LIDAR Cloud Classification from CPR & CALIOP CloudSat products

Frequency distributions of COT, CTP, Rainfall TMPA (Korea) TMPA (US) AWS (Korea)

Frequency distributions of IWC and LWC # 46,678 # 91,939 # 31,634 # 12,766 CloudSat IWC (mg/m 3 ) Height (km) LWC (mg/m 3 ) Height (km) IWC (mg/m 3 ) Height (km) LWC (mg/m 3 ) Height (km) ice water ice water

Cloud classification using K-means clustering Cloud Regime Classification ISCCP “cloud regimes” defined by K-means clustering algorithm for 15°N-15°S (Rossow et al., 2005) applied to cloud top pressure (CTP) - cloud optical thickness (COT) histograms Deep convective AnvilCongestus Thin cirrus Shallow Cu Marine Sc [Rossow et al., 2005, GRL] ISCCP Cloud Classification Cluster Analysis (15N – 15S)

Cluster Analysis K-means clustering algorithm A method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. 1. Select k initial “means” 2. k clusters are created by associating every observation with the nearest mean 3. The centroid of each of the k clusters becomes the new means 4. Step 2 and 3 are repeated until convergence has been reached.

Deep convectionCumulus convectionLow cloud CirrusAnvil 1Anvil 2 US= 28% Korea= 34% US (TMPA): 3.2 mm/day Korea (TMPA): 8.7 mm/day Korea (AWS): 8.9 mm/day US= 8% Korea= 20% US (TMPA): 9.7 mm/day Korea (TMPA): 27.8 mm/day Korea (AWS): 25.8 mm/day US: 32% Korea: 18% US (TMPA): 0.1 mm/day Korea (TMPA): : 0.4 mm/day Korea (AWS): 1.3 mm/day US: 10% Korea: 17% US (TMPA): 1.9 mm/day Korea (TMPA): 3.2 mm/day Korea (AWS): 2.4 mm/day US: 11% Korea: 5% US (TMPA): 0.1 mm/day Korea (TMPA): 0.7 mm/day Korea (AWS): 0.6 mm/day US: 11% Korea: 6% US (TMPA): 0.8 mm/day Korea (TMPA): 1.2 mm/day Korea (AWS): 1.5 mm/day Rain features with weather states

Column water vapor Deep convection Cumulus convection Deep convection Cumulus convection Geopotential Height (Solid), Temperature (Dash), Wind (Vector) at 850 hPa KoreaUS

Moisture flux and K index Deep convection Cumulus convection Deep convection Cumulus convection Geopotential Height (Solid), Temperature (Dash), Moisture Flux (Vector) at 850 hPa Korea

Wind shear (V 200hPa – V 850hPa ) Deep convection Cumulus convection Deep convection Cumulus convection Geopotential Height (Solid), Temperature (Dash), Wind (Vector) at 500 hPa

TMI, AWS 267K, 500mb MODIS, AWS

COT>50 Cold (CTP < 500 hPa) Warm (CTP > 500 hPa) Geopotential Height (Solid), Temperature (Dash), Wind or Moisture Flux (Vector) 850hPa 500hPa

COT>50 Cold (CTP < 300 hPa) Warm (CTP > 300 hPa) Geopotential Height (Solid), Temperature (Dash), Wind or Moisture Flux (Vector) 850hPa 500hPa

COT>50 Cold (CTP < 200 hPa) Warm (CTP > 200 hPa) Geopotential Height (Solid), Temperature (Dash), Wind or Moisture Flux (Vector) 850hPa 500hPa

Low Thick Cloud # Rainfall (mm/day) COTRe CWP (g/m 2 ) CTP (hPa) CTT (K) CF (%) COT > 20 CTP > 500 hPa COT > 50 CTP > 500 hPa COT > 50 CTP > 300 hPa COT > 50 CTP > 200 hPa High Thick Cloud # Rainfall (mm/day) COTRe CWP (g/m 2 ) CTP (hPa) CTT (K) CF (%) COT > 20 CTP < 500 hPa COT > 50 CTP < 500 hPa COT > 50 CTP < 300 hPa COT > 50 CTP < 200 hPa

Summary 미국과는 매우 다른 한국의 강수기구 - 키가 낮은 Warm cloud 에서도 강한 강수유발 - Ice crystal 의 양이 적게 나타남 - 대류불안정 보다는 경압성이 큰 구조의 강수 기구 - 계절변동과 유사한 강수구조의 변동성 유발하는 문제점 - Rain retrieval algorithm developed for US never works over Korea - Same can be applied to radar remote sensing (possibly) - Cumulus parameterization scheme working well over US may not work over Korea, same as shown in the rain retrievals (Empirically known from various numerical simulation studies, or from diagnostics studies using reanalysis data).

Low Thick Cloud : High Thick Cloud COT > 20, CTP < 500 hPa COT > 20, CTP > 500 hPa Geopotential Height (Solid), Temperature (Dash), Wind or Moisture Flux (Vector) 850hPa 500hPa

prospect and outlook