Basis of GV for Japan’s Hydro-Meteorological Process Modelling Research GPM Workshop Sep. 27 to 30, Taipei, Taiwan Toshio Koike, Tobias Graf, Mirza Cyrus Raza, Thomas Pfaff, University of Tokyo and JAXA

2 Overview Remote Sensing of Solid Precipitation  Ground Based Radiometer  Observation of Snowfall over the Ocean Cloud Microphysics Data Assimilation System Cloud Microphysics Data Assimilation System GV Needs GV Needs

3 Methodology Physical Based Retrieval of Snowfall: Minimizing the difference between modeled and observed brightness temperature data.Minimizing the difference between modeled and observed brightness temperature data. Consider all parameters effecting radiative transfer.Consider all parameters effecting radiative transfer.

4 Model Parameterisation Many parameters need to be considered in RTM, which can be derived from additional data sources: Humidity, Pressure,Temperature: Observation, NWP Model Output/AIRSHumidity, Pressure,Temperature: Observation, NWP Model Output/AIRS Cloud Position: Satellite Observation in Infra-red Region, CeilometerCloud Position: Satellite Observation in Infra-red Region, Ceilometer Boundary Condition:Boundary Condition: Ocean => Wind SpeedOcean => Wind Speed SpaceSpaceMissing: SnowfallSnowfall Cloud WaterCloud Water

5 Snow Water Path/Cloud Water TB observation is only integrated view of all parameters can't get Profile of Snowfall and Cloud Water can't get Profile of Snowfall and Cloud Water assume uniform profile (integrated snowfall) assume uniform profile (integrated snowfall) Model Parameterisation Reality Model

6 Wakasa Bay 2003 Application: AMSR/AMSR-E Validation Project AMSR/AMSR-E Validation ProjectData: Humidity, Temperature & Pressure => Global Reanalysis (GANAL) Data, Radio SondeHumidity, Temperature & Pressure => Global Reanalysis (GANAL) Data, Radio Sonde Cloud Top => MODIS Product, GMSCloud Top => MODIS Product, GMS Wind Speed => AMSR-E ProductWind Speed => AMSR-E Product Brightness Temperature => AMSR-E, Ground Based RadiometerBrightness Temperature => AMSR-E, Ground Based Radiometer Comparison with Radar Observation and Gauge Data

Ground-Based Radiometer Snowfall Observation

8 Radiative Transfer Simulations Lookup Table Ground Based Observation Snowfall Rate Cloud Liquid Water ● Relative Humidity, Temperature and Pressure Profile ● Cloud Top and Bottom Passive Microwave Brightness Temp. at 36.5 and 50.8 GHz <= Radiosonde <= fixed (1000 m ＆ 3000 m) Atmospheric ParameterMethodology

9 Results: Problem: Time Gap between Radiometer and Gauge Results Snow Retrieval Validation

10 Consider Cloud Movement Radar images at 2000 m gauge site radiometer view point

11 Averaged Snowfall Results Good agreement within the range of uncertainty when averaged over periods of cloud scale movement

Satellite Snowfall Observation over Ocean

13 Snowfall - Satellite [g/m 2 ] Results: Snowfall – Jan. 29, 2003 at 03:31z Snowfall - Radar [g/m 2 ] Similar pattern can be observed results are slightly shiftedresults are slightly shifted results are more spreadresults are more spread

14 Scatter – Plot Shift between Radar & Satellite: R 2 = 0.69

15 Problems Slant Path: AMSR-E observation at anAMSR-E observation at an incident angle of 55º Snowfall:Snowfall:  Blur Snowfall  Shift of results Footprint Size & Cloud Heterogeneity (36.5 and 89 GHz) => Beam Filling Problem

16 Reasonable results for both approaches, but: at the moment only integrated snowfall content (uniform snowfall rate) possibleat the moment only integrated snowfall content (uniform snowfall rate) possible Problems due to cloud heterogeneity and cloud movementProblems due to cloud heterogeneity and cloud movementSummary

Cloud Microphysics Data Assimilation System Assimilation System

18 CMDAS/IMDAS Approach IF J min No Yes ARPS Model Output (Initial Guess) Observation Operator (RTM) (Tb mod ) Model Operator (Assim. Parameter:ICLWC, IWV) Cost (J)= (Tb mod - Tb obs ) 2 Global Minimization Scheme (Shuffled Complex Evolution) Duan et al, 1992 Optimized Initial Condition Cloud Parameter Update Precipitation Estimation by ARPS

19 AMSR-E Product Assimilation Result Cloud Water Content – Jan 25 th

20 Analysis IMDAS IWV CMDAS IMDAS ICLWC CMDAS 29th July, IMDAS IWV CMDAS IMDAS ICLWC CMDAS 25th July, 2003 RMSE Co-eff. Of Correlation Assim. Parameter Assim. SystemDate

21 29 th Jan, 20:00z Precipitation Rate(mm/hr) No Assimilation Assimilation

22 Summary CMDAS & IMDAS both improve the performance of cloud microphysics scheme significantly  heterogeneity into external GANAL data,  Improved atmospheric initial conditions With improved IC by assimilation systems, ARPS model has improved the estimation of cloud distribution & short range precipitation forecast but its over estimated at few places.

23 GV Needs Comprehensive Atmospheric Data Set for Application and Validation of Algorithms and RTMComprehensive Atmospheric Data Set for Application and Validation of Algorithms and RTM Water Vapour and Cloud Water Content ProfilesWater Vapour and Cloud Water Content Profiles RTM: Detailed Information of solid precipitation (type, drop size distribution etc.)RTM: Detailed Information of solid precipitation (type, drop size distribution etc.) Snowfall Profiles => Radar  Observation liquid  solidSnowfall Profiles => Radar  Observation liquid  solid Precise (spatial) Information about cloud coverPrecise (spatial) Information about cloud cover

25 Basic Concept Satellite only provides observation during overpass => Continuous Representation of Precipitation => Data Assimilation Satellite only provides observation during overpass => Continuous Representation of Precipitation => Data Assimilation Data Assimilation of Cloud Water and Water Vapor => (Solid) Precipitation in Future

26 Assimilation Window ARPS Model Simulation 16:30z 16:30z 17:10z Assimilation Window: 40 mins TB obs AMSR-E Initial Guess 29 th Jan th Jan 2003