Intercomparison of Satellite Precipitation Products for Different Cloud Types

Transcription

1 8 th IPWG & 5 th IWSSM Joint Workshop Bologna, 3-7 October, 2016 Intercomparison of Satellite Precipitation Products for Different Cloud Types Nobuyuki UTSUMI Hyungjun Kim, Taikan OKI (IIS, The University of Tokyo) 1 utsumi@rainbow.iis.u-tokyo.ac.jp

2 Satellite precipitation products Product KuPR L2 (ver3) GMI L2 (ver3) Resolution Instantaneous/On-track Instantaneous/On-track GSMaP MVK (ver.6) 1-hourly / 0.1 deg. (ver.6) (IR) (ver.6) (MW) IMERG (ver.3) 0.5-hourly/ 0.1 deg. JAXA/EORC (ver.3) (IR) (ver.3) (MW) Radar-AMeDAS 1-hourly / ~1km JAXA/EORC 2 utsumi@rainbow.iis.u-tokyo.ac.jp

3 Cloud type data Satellite Cloud Grid Information over Western North Pacific by Japan Meteorological Agency Visible and Infrared (IR) from Himarwari deg x 0.25 Pacific Every 1 hour Representative cloud type (this study) Total cloud Cloudy Deep Conv. I >0.1 >0.5 Deep Conv. II 0.1 <Cb < =0.5 Cloud amount High <=0.1 >0.5 Cloud amount ( ) Total cloud High Cumulonimbus Mid & Low (Others) Cumulonimbus High Mid & Low Mid & Low <=0.1 >0.5 Mixed <=0.1 <=0.5 <=0.5 Clear sky <=0.1 3 utsumi@rainbow.iis.u-tokyo.ac.jp

4 Cloud type 4 utsumi@rainbow.iis.u-tokyo.ac.jp High cloud Deep Convection (I & II) Mid & Low cloud

5 Cloud type 5 utsumi@rainbow.iis.u-tokyo.ac.jp 35% 30% Deep Convection (I & II) 25% 20% 15% 10% 5% 0% 0.1% 0.4% High cloud Mid & Low cloud

6 6 Precipitation Classification was performed every 1-hour Method = Cloud type Precipitation by each cloud type Over the region around Japan (< 40 o N) 2014/4 2015/6 (w/o Nov.-Mar.) Compared at 1-hourly, 0.20 x 0.25 deg. Only over the sea

7 7 mm/h RA KuPR GMI all IR MW <--- IMERG ---> all IR MW <--- GSMaP ---> Precipitation rate [mm/h] Deep Convection I KuPR & IRs Overestimation mm/h IR High Most products (e.g. IMERG IR) Underestimation --- IMERG GSMaP --- Mid & Low IR estimates Underestimation

8 8 --- IMERG GSMaP --- Total Precipitation [mm/month] High Large contribution --- IMERG GSMaP --- Deep conv. / Mid & Low Small contributions Deep conv Mid & Low

9 [mm/month] -- IMERG GSMaP -- Bias (to Radar-AMeDAS) Mid & Low Large contribution to total bias (despite smaller total precip.) KuPR Deep Conv. I Overestimation High, Mid & Low Underestim. GMI Deep Conv. I Overestimation High, Mid & Low Underestim. IMERG High, Mid & Low Underestim. GSMaP High good Mid & Low Underestimation 9 utsumi@rainbow.iis.u-tokyo.ac.jp

10 Probability density function (all cloud types) RA GSMaP IMERG(IR) KuPR KuPR Precipitation rate [mm/h] 10

11 Probability density function (each cloud type) 11 Deep Conv. I GSMaP GSMaP (IR) IMERG(IR) Deep Conv. II High Mid & Low

12 Probability density function (each cloud type) 12 Deep Conv. I GSMaP GSMaP (IR) IMERG(IR)

13 Satellite [mm/h] 13 Comparison against Radar-AMeDAS (Deep Conv. I) IMERG (MW) sea IMERG (IR) Sea Radar-AMeDAS [mm/h] Radar-AMeDAS [mm/h] PDF CDF PDF

14 Satellite [mm/h] Comparison against Radar-AMeDAS (Deep Conv. I) GSMaP (MW) Sea GSMaP (IR) Sea Radar-AMeDAS [mm/h] Radar-AMeDAS [mm/h] PDF CDF IRs are overestimation for weak precipitation of Deep conv. 14

15 15 Conclusion Deep Convection KuPR & IRs Overestimation (precip. rate) IR estimates Overestimation for weak precip. High Most products (particularly IMERG IR) Underestimation (precip. rate) Mid & Low Large source of biases despite its smaller total precip. IR estimates Notable underestimation

16 Conclusion KuPR Deep Conv. Overestimation (vs Radar-AMeDAS) High, Mid & Low Underestimation GMI Deep Conv. Overestimation High, Mid & Low Underestimation IMERG Deep Conv. IR shows overestimation for weak precip. High, Mid & Low Underestimation (particularly IR) GSMaP Deep Conv. IR shows overestimation for weak precip. High good Mid & Low Underestimation (particularly IR) 16