Characteristics of High-Resolution Characteristics of High-Resolution Satellite Precipitation Products Satellite Precipitation Products

in Spring and Summer over Chinain Spring and Summer over ChinaYan Shen1, A.-Y. Xiong1

Pingping Xie2

1. National Meteorological Information Center (NMIC), China Meteorological Administration (CMA), Beijing,100081

2. NOAA Climate Prediction Center, Camp Springs, MD, 20746

Oct. 15,2008,

At the 4th Workshop of the International Precipitation Working Group (IPWG)

ObjectivesObjectives

• To generate gauge-based analysis of HOURLY

precipitation with the daily optimal interpolation(OI)

algorithm using station data over China

• To examine the performance of six hi- resolution

satellite-based products in depicting hourly

precipitation

• To introduce the daily precipitation analysis

operational system in NMIC

The Gauge-Based AnalysisThe Gauge-Based Analysis

The Gauge AnalysisThe Gauge Analysis

Hourly gauge data from ~2400 stations

Hourly precipitation analysis on a 0.25o over the China

Currently hourly analyses constructed for 3-year period from 2005 to 2

007

Interpolated through the optimal interpolation (OI) algorithm develope

d by Xie et al. (2006)

A two-step approach: First to interpolate the ratio of total

hourly rain to daily climatology through the OI and then to

define the total by multiplying the ratio with daily climatol

ogy

Correction for the orographic effects through employment

of the PRISM climatology

Sample hourly analysisSample hourly analysis for 11Z,June20,2005

This analysis includes the precipitation rate and gauge number distribution information

According to the gauge density information, user can determine whether or not they use it over a place

Validating Six Hi-Resolution Validating Six Hi-Resolution Satellite Estimates Using the Satellite Estimates Using the

Gauge AnalysisGauge Analysis

Verified Satellite Precipitation ProductsVerified Satellite Precipitation Products

• COMB

• CMORPH

• PERSIANN

• NRL-Blended

• TRMM 3B42RT

• TRMM 3B42 / MPA

• Comparison Period: 3 years from 2005 to 2007; only include Spring (AMJ) and Summer (JAS)

• Temporal / Spatial Resolution: 3-hourly / 0.25o×0.25o

Seasonal Mean Precipitation in Spring Seasonal Mean Precipitation in Spring ((Apr.-Jun.Apr.-Jun.))

All satellite estimates can capture the overall structures of precipitation

Satellite estimates tend to generate smoother distribution patterns with regional biases compared to hourly gauge analysis

Satellite estimates adjusted by gauge data (TRMM/3B42) and CMORPH product present the closest to the gauge analysis

The PERSIANN exhibits large over-estimates of precipitation over Tibetan Plateau

NRL, COMB and TRMM/3B42RT have an over-estimation precipitation near the southeast Tibetan plateau

Seasonal Mean Precipitation in Summer Seasonal Mean Precipitation in Summer ((Jul.-Sep.Jul.-Sep.))

All satellite estimates can capture the overall structures of precipitation

Satellite estimates adjusted by gauge data (TRMM/3B42) presents the closest to the gauge analysis

The PERSIANN exhibits large over-estimates of precipitation over Tibetan Plateau

NRL and TRMM/3B42RT have an over-estimation precipitation near the southeast Tibetan plateau

Serial Correlation Serial Correlation ((3-hourly3-hourly for for SpringSpring))

Correlation between every satellite products and gauge analysis has similar pattern with high over eastern China but relatively poor over western arid China;

CMORPH has the highest correlation with the gauge analysis,

especially in the eastern China

Serial Correlation Serial Correlation ((3-hourly3-hourly for for SummerSummer))

The same distribution characteristics as the spring ones with high over eastern China but relatively poor over western arid China;

CMORPH has the highest correlation with the gauge analysis,especialy in the eastern China

Serial Bias Serial Bias ((3-hourly3-hourly for for SpringSpring))

Every satellite products have bias in different regions over China with negative bias over eastern wet regions and relatively positive

bias over western arid area;

Gauge-adjusted TRMM/3B42 has the smallest bias with the gauge analysis over the China region

mm/day

Serial Bias Serial Bias ((3-hourly3-hourly for for SummerSummer))

With negative bias over eastern wet

regions and Tibetan Plateau for all the products except the

PERSIANN data. PERSIANN has an overestimation

trend;

Gauge-adjusted TRMM/3B42 has the smallest bias with the gauge analysis over the China region mm/day

Time Series of Bias and Pattern CorrelationTime Series of Bias and Pattern Correlation

Correlation improves with the seasonal advance and reaches to a stable level from the April and worsens from the October;

CMORPH presents best performance consistently throughout the period;

Bias exists and changes over time

Bias and Correlation Coefficients between gauge Bias and Correlation Coefficients between gauge observation and satellite estimatesobservation and satellite estimates

in different seasonsin different seasons

Satellite Products

All months Spring Summer

Bias(%)

Corr Bias(%) Corr Bias(%) Corr

CMORPH

-21.23

0.604 -10.75 0.642 -16.01 0.597

PERSIANN

-26.22

0.416 -10.46 0.458 -28.09 0.408

COMB -21.89 0.536 -11.47 0.566 -16.49 0.536

NRL -16.29 0.445 -6.50 0.473 -16.73 0.446

3B42 -0.51 0.527 0.52 0.549 2.25 0.542

3B42RT -15.14 0.502 -5.14 0.523 -11.59 0.509

PDF of PDF of [3-Hourly][3-Hourly] Precipitation Precipitation• Frequency of No-Rain Events

• Gauge Analysis: 82.9%

• CMORPH: 79.6% PERSIANN: 85.5%

• COMB: 88.7% NRL: 83.6%

• 3B42: 90.2% 3B42RT: 89.9%

• Frequency of Events with Rain

0. 00

0. 02

0. 04

0. 06

0. 08

0. 10

0. 12

0. 14

0. 16

0. 18

0<r<=1. 0 1. 0<r<=2. 0 2. 0<r<=5. 0 5. 0<r<=10. 0 10. 0<r<=15. 0 r>15. 0

Preci pi tati on Range (mm/ hr)

Freq

uen

cy

GAG CMP PER COM

NRL MPA 3RT

Operational System of Operational System of Daily precipitation analysisDaily precipitation analysis

Flow Chart of this systemFlow Chart of this system

T

Retrieve dailyObservations

Quality Control

Climatology field

Calculate RatioRatio analysis field

Precipitation analysis:

= ×

= ×

)(' tR

),,,( tzyxR ),,( zyxR ),,(' tyxR

RtRtR /)()('

),,('),,(),,,( tyxRzyxRtzyxR ),,( zyxR Service

Data format: GrADS/ ArcGIS/GIF

This analysis includes the precipitation rate and gauge number distributi

on information Three data formats

including GrADS, ArcGIS and GIF are offered to users

According to the gauge density information, user can determine whether or not

they use it over a place

Data available to the usersData available to the users

CDC website :

http://cdc.cma.gov.cn/shishi/pre_grid0.25.jsp

Data format : GrADS， ArcGIS and Gif

Data search way : format + time

Temporal/spatial resolution : daily/0.25deg

MORE

TURN BACK

From August 1 to September 8, 2008

CONCLUSIONSCONCLUSIONS• Taking advantage of a dense gauge network over China, a gauge-

based analysis of hourly precipitation has been constructed;• The gauge analysis is applied to examine the performance of

hi-resolution satellite precipitation estimates in different seasons and different parts of China on a sub-daily time scale;

• The daily precipitation analysis system has been put into operation in the National Meteorological Information Center (NMIC) in China Meteorological Administration (CMA);

• Further work is to develop a new objective system to construct high-resolution precipitation analysis by merging gauge observations and satellite estimates.

THANKS !

ANY QUESTIONS?

My E-mail: sheny@cma.gov.cn

syzld8@hotmail.com