# Presentation: 823848

**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**
**tgraf@hydra.t.u-tokyo.ac.jp**

## Overview 

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

## Methodology

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

## 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/AIRS
- Cloud Position: Satellite Observation in Infra-red Region, Ceilometer
- Boundary Condition:
- Ocean => Wind Speed
- Space
- 		Missing:
- Snowfall
- Cloud Water

## Snow Water Path/Cloud Water

- 	TB observation is only integrated view of _all_ parameters
- can't get Profile of Snowfall and Cloud Water
- assume uniform profile (integrated snowfall)
- Model Parameterisation

## Wakasa Bay 2003

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

## Ground-Based Radiometer Snowfall Observation

## Methodology

- 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)

## Results:

- Problem: Time Gap between Radiometer and Gauge Results

- Snow Retrieval Validation

## Consider Cloud Movement

- Radar images at 2000 m

- gauge site

## Averaged Snowfall Results

- Good agreement within the range of uncertainty when averaged over periods of _cloud scale movement_

## Satellite Snowfall Observation over Ocean

## Results:	Snowfall – Jan. 29, 2003 at 03:31z

- Results:	Snowfall – Jan. 29, 2003 at 03:31z

- 		Similar pattern can be observed
- results are slightly shifted
- results are more spread

## Scatter – Plot

- Shift between Radar & Satellite:

- R2 = 0.69

## Problems

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

## 	Reasonable results for both approaches, but:

at the moment only integrated snowfall content (uniform snowfall rate) possible

Problems due to cloud heterogeneity and cloud movement

- at the moment only integrated snowfall content (uniform snowfall rate) possible
- Problems due to cloud heterogeneity and cloud movement

- Summary

## 
Cloud Microphysics Data
 Assimilation System

- Cloud Microphysics Data
-  Assimilation System

## CMDAS/IMDAS Approach

** **

**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**

**Notes:**
CMDAS = Warm-Kessler
IMDAS  = Ice-Microphysic Scheme from Lin

## AMSR-E Product

- Assimilation Result

**Cloud Water Content – Jan 25****th**** **

## Analysis

## 29th Jan, 20:00z

**29****th**** Jan, 20:00z**

**Precipitation Rate(mm/hr)**

- No Assimilation

- Assimilation

**Notes:**
Results showing that IC still not deteriorated after 3 hrs 30 mins and still giving us such a heterogeneous structures.

## 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.**

## GV Needs

- Comprehensive Atmospheric Data Set for Application and Validation of Algorithms and RTM
- Water Vapour and Cloud Water Content Profiles
- RTM: Detailed Information of solid precipitation (type, drop size distribution etc.)
- Snowfall Profiles => Radar  Observationliquid  solid
- Precise (spatial) Information about cloud cover

## Basic Concept

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

## Assimilation Window