China Regional Reanalysis : One-year Preliminary Experiments and - - PowerPoint PPT Presentation

China Regional Reanalysis : One-year Preliminary Experiments and Evaluation of First Stage (1998 ~ 2007) Datasets

Qi Zhang, Jianping Tang, Yinong Pan

School of Atmospheric Science, Nanjing University

2018/7/15

Outline

Ø Motivation Ø CNRR Experiments Design Ø Evaluation of 1-Year Period Experiments Ø Preliminary Results of 10-Year CNRR Ø Conclusions

Motivation

East Asia especially China is an area suffering many kinds of natural hazard caused by extreme atmospheric events. Severe Convective Systems Tropical Cyclone Snow Storm Heat Wave

High temporal and spatial resolution of atmospheric best estimation is needed!

What is Regional Reanalysis

• 1. Based on data assimilation method and regional model
• 2. Driven by the global or regional reanalysis
• 3. Provide higher resolution gridded reanalysis data

North American Regional Reanalysis (NARR)

Name Time Region Resolution NARR (2006) 1979-now North American 36km 3hourly ASR ( 2016) 2000-2010 Arctic 15km 3hourly EURO4M (2013) 1989-2010 Europe 0.2degree 6hourly IMDAA (2014) 2014 – 2017 South Asia 12km 6hourly EARR (2017) 2013-2014 East Asia 12km 6hourly

regional Arctic System Reanalysis (ASR) EURO4M regional reanalysis system

Prototype CNRR Workflow Design

Model Configuration

Model prototype WRF Governing equations Nonhydrostatic Grids and resolution 481x361, 18km Vertical Layers (top) 45 sigma layers (10hPa) Cumulus convection Kain-Friticsh Explicit moisture WSM5 PBL MYJ Radiation RRTMG Land Surface NOAH LSM DA Scheme 3Dvar

Conventional Observation Data

CFSR ingest datasets with China local sounding data added

Satellite Radiance Data

CFSR ingest datasets

Initial and Boundary Condition

ERA-Interim 6-hourly globab reanalysis in pessure level

Sea Surface Temperature

NCEP Daily OISST

Data Used by CNRR

Evaluation of 1-Year Period Experiment based on 2013

p Test the performance of CNRR with GSI method p Compare the GSI and Spectral Nudging (SN) in CNRR

One-Year (2013) CNRR Experiments

2012-12-01 00 UTC 2013 12-31 18 UTC Every 6 hour

......

CNRR-CTL CNRR-SN CNRR-GSI CNRR-GSN

2012-12-01 00 UTC 2013 12-31 18 UTC Every 6 hour

GSI Spectal Nudging

2012-12-01 00 UTC 2013 12-31 18 UTC Every 6 hour

GSI

2012-12-01 00 UTC 2013 12-31 18 UTC Every 6 hour

Spectal Nudging

...... ...... ...... Traditional downscaling S p e c t r a l N u d g i n g ( S N i n s h

• r

t ) Data Assimilation (GSI in short) Hybrid method (GSISN) Technical route We have tested in Preliminary Experiments

Statistical Parameters of 2m AGL Temperature （Bias, RMSE, CORR）

Statistical Parameters of Daily Precipitation （Bias, RMSE, CORR）

Ø CNRR can reproduce the spatial-temporal distributions of near surface meteorological variables

(a) (b) (c)

Heat Waves in 2013

Spatial Correlations of GHT at 500 hPa Ø The GSI methods can accurately simulate the heat waves during the summer of 2013 by improving the reproduction of large-scale circulations

RMSE Profile of upper level variables

Ø The analysis of the CNRR experiments with the GSI method (CNRR-GSI, CNRR-GSN) is closer to the sounding observation than the ERA-Interim reanalysis for the specific humidity at low troposphere and winds at middle and high troposphere

Preliminary Results of 10-Year CNRR

ü Evaluation of the 10-year CNRR ü Compare CNRR GSI methods assimilated with different dataset

10-Year CNRR Experiments: Workflow

1997010100 1997010106 2007123118 2008010100

Data Assimilation Initial condition

Hincast

Every 6 hour Cycle

1997010100 1997010106 2007123118 2008010100

Hincast

Every Time Step

Spectral Nudging Nudging Informatio

CNRR_GSIERA CNRR_SNUVTQ

6 hour Hindcast

1997010100 1997010106 2007123118 2008010100

Data Assimilation Initial condition

Hincast

Every 6 hour Cycle

CNRR_GSIERA

6 hour Hindcast

Initial Condition generated from ERA-Interim Observation ERA-Interim

Overview of Monthly Precipitation

CNRR-GSISAT CNRR-GSIERA CNRR-SNUVTQ CMORPH CN05.1 Data assimilation (DA) with observation’s performance is the best especially in ocean regions, DA with reanalysis is inferior but better than spectral nudging.

Frequency Distribution of Daily Precipitation

Under-estimation of precipitation occurrence is relieved in data assimilation experiments; DA with reanalysis performs better in extreme precipitation events.

Statistical Parameters of Daily Precipitation （Bias, RMSE, CORR）

DA with Observation is best and DA with reanalysis has significant improvement in simulating summer precipitation

Temporal Variations of Daily Precipitation and Temperature

Precipitation Daily mean Temperature Near surface temperature’s performance difference is identical to Precipitation Data assimilation experiments exhibit performance enhancement in South and Southwest China

Upper Level Variables: 10-year averaged spatial Bias RMSE and CORR

Upper Level Variables’ 5-day mean RMSE

U wind V wind Temperature Specific humidity Geographical height

Conclusions

Ø Assimilating satellite radiance and conventional radiosonde data into a WRF modeling system every 6 h during the study period, thus providing a physically and dynamically consistent regional climate estimate in China. Ø Assimilating reanalysis dataset in replacement of authentic observation is proved to be a feasible approach because this method performs better than spectral nudging. Ø Considering computational consumption, conducting regional downscaling by using reanalysis-data-assimilated method is a win-win decision for the fact that it takes relatively small computational and storage resources but achieves promising performance.