Satellite Soil Moisture Content Data Assimilation in Operational Local NWP System at JMA

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1 Satellite Soil Moisture Content Data Assimilation in Operational Local NWP System at JMA Yasutaka Ikuta Numerical Prediction Division Japan Meteorological Agency Acknowledgment: This research was supported by JAXA Microwave Science Team Joint Workshop of the 2nd International Surface Working Group (ISWG) and 8th Land Surface Analysis Satellite Application Facility (LSA-SAF) Workshop in Lisbon, Portugal on the 27 June,

2 Operational NWP system in JMA Global NWP System Global Spectral Model (GSM) Horizontal resolution:tl959( deg) Global Analysis (GA): 4D-Var Meso-Scale NWP System Meso-scale model (MSM) Horizontal resolution: 5 km Meso Analysis (MA): 4D-Var Local NWP System Main purposes Disaster Risk Reduction Aviation Forecast Local Forecast Model (LFM) Horizontal resolution: 2 km Highest resolution model in JMA Local NWP System Local Forecast model (LFM) Local Analysis (LA): 3D-Var Analysis cycle Initial time 3 h Local Analysis (LA) Initial time Forecast(1h) Forecast(1h) Forecast(1h) Forecast 3DVAR 3DVAR 3DVAR Horizontal resolution: 5 km Three dimensional variational (3DVar) data assimilation + 1 hour forecast cycle 3DVAR 2

3 Current Status of observations assimilated in Local Analysis Conventional Observation Data Surface(Ps, T, Qv, Wind) AMeDAS*(T,Wind) SHIP,BUOY(Ps) Aviation(T,Wind) *AWS Ground-based Doppler Radar (Radial wind) Sonde(Ps,T,RH,Wind), WPR(Wind) Ground-based Doppler Radar (Relative humidity) 3

4 Current Status of observations assimilated in Local Analysis Satellite Observation Data Soil Moisture Content Metop-A/ASCAT Metop-B/ASCAT GCOM-W/AMSR2 Soil Moisture Content [since Jan 2017] GCOM-W AMSR2, Metop-A/B Himawari-8/AHI Radiance GPM/GMI Radiance [since Jan 2017] Himawari-8 AHI, GPM GMI, GCOM-W AMSR2, Metop-A/B AMSU-A/MHS and DMSP SSMIS GNSS Precipitable Water vapor Himawari-8/AMV Ground based GNSS PWV Metop-A/AMSU-A Metop-A/MHS AMV 4

5 Satellites Radiance Himawari-8 AHI, GPM GMI, GCOM-W AMSR2, Metop-A/B AMSU- A/MHS and DMSP SSMIS Observation operator RTTOV 10.2 Bias Correction Method Variational Bias Correction (adaptive) Scan Bias Correction (statistic) 5

6 Satellites Soil Moisture Content GCOM-W AMSR2 and Metop-A/B ASCAT Observation operator Simple regression using CDF (Cumulative Distribution Function) matching Bias Correction Method Variational Bias Correction (adaptive) CDF Matching (statistic) 6

7 Variational Bias Correction in Local Analysis Cost function Background term Estimated observation bias Observation term Bias correction term Error cov. S for VarBC control variables Diagonal matrix 1 S N 2 sys 2 d var N N var 2 1 N0=1000 N N N0 Nvar log 10N N0 1 N0 N N0 Sato (2006), Ishibashi(2006) Number of obs. 7

8 Analysis variable Soil Elements in Local Analysis l: liquid, s: solid, a: air Skin and soil temperature Upper air observation eg. Sonde, aviation and WPR Soil volumetric water content Level = 2 Gradient of observation operator To atmosphere To surface and under ground v _ 1.5m 1.5 m T, Qv observation v _ suf C C h h z z BOA 1.5m C C m m z 1.5m v _ BOA v _1.5m z BOA Level = 1 Level = m Bottom of Atmosphere Surface Ts, Tg, Wg observation UG Level = 1 8

9 Simulation of soil moisture content observation mˆ Observation operator for SMC a b s w g CDF matching approach (Dharssi,2011) Average of simulated SMC mˆ s m s mˆ s m s Regression equation mˆ s ms Simulated SMC m w s g Average of observed SMC Average of model Wg w g Model Wg w g 9

10 Spatial Error Correlation Expected value of Error covariance Spatial correlation of BG error for Wg Assumption: Horizontal error correlation of Wg in BG. Horizontal error correlation of observation 10

11 Spatial Error Correlation GCOM-W AMSR2 Metop-A ASCAT Correlation is seek by the steepest descent method. GCOM-W AMSR2: rc=4.1 km, C < 0.2 when r > 10 Metop-A ASCAT: rc=11.1 km, C < 0.2 when r > 20 Thinning Interval of SMC: 25 km 11

12 Quality Control of SMC Observation Accuracy of SMC by satellite observation products depends on status of land surface. QC based on occurrence conditions of large bias Precipitation area Reject by ground-based radar observation (Radar Analysis). Snow area Reject by the Snow Depth Analysis (OI). Forest, urban and surface water area Reject by the National Land Numerical Information by the Geospatial Information Authority of Japan. GCOM-W AMSR2 Coverage of SMC that pass QC. Rain Analysis 12

13 Quality Control of SMC Observation Accuracy of SMC by satellite observation products depends on status of land surface. QC based on occurrence conditions of large bias Precipitation area Reject by ground-based radar observation (Radar Analysis). Snow area Reject by the Snow Depth Analysis (OI). Forest, urban and surface water area Reject by the National Land Numerical Information by the Geospatial Information Authority of Japan. GCOM-W AMSR2 Coverage of SMC that pass QC. Clear Sky Radiance 13

14 Innovation histograms before qc: using QC flag of L2 product after qc: before qc+reject rain, snow, forest, urban and surface water Rain events Observation with fatal error is removed by QC. Bias is further removed by CDF matching and VARBC. 14

15 Observation Analysis increment of Wg Impact of surface observation on the under ground is limited. Surface + SMC Surface observation and SMC mutually complement each other. 15

16 Forecast verification in summer Precipitation forecast Very small impact. Verification period: 2015/07/ /07/26, 2015/08/ /08/04, 2015/08/ /09/10 16

17 RMSE RMSE RMSE RMSE Forecast verification in summer 1.5 m Temperature Positive impact 1.5 m Qv Negative impact Verification period: 2015/07/ /07/26, 2015/08/ /08/04, 2015/08/ /09/10 17

18 Error of surface water vapor Negative impact of surface Qv Surface Qv is improved at initial time by SMC assimilation. However negative bias is increasing near surface rapidly. Reasons of the bias of near surface: Bias of lower atmosphere. Error from assumption of modeling. 18

19 Summary JMA started to assimilate satellite observations in the operational Local NWP system in Jan Clear sky radiance Himawari-8 AHI, GPM GMI, GCOM-W AMSR2, Metop-A/B AMSU-A/MHS and DMSP SSMIS Soil moisture content GCOM-W AMSR2, Metop-A/B ASCAT Impact of Soil moisture content data assimilation in LFM Surface Temperature: positive impact Surface Water vapor: negative impact Bias of lower atmosphere and error of assumption of modeling. Future plans Assimilation of other satellite products Implementation of SMC assimilation method to Meso-scale NWP system Development to reduce the bias in lower atmosphere. 19

20 THANK YOU FOR YOUR ATTENTION 20