Characteristics of High-Resolution Satellite Precipitation Products in Spring and Summer over China Yan Shen 1, A.-Y. Xiong 1 Pingping Xie 2 1. National Meteorological Information Center (NMIC), China Meteorological Administration (CMA), Beijing, NOAA Climate Prediction Center, Camp Springs, MD, Oct. 15,2008, At the 4th Workshop of the International Precipitation Working Group (IPWG)

Objectives 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 Analysis

The Gauge Analysis  Hourly gauge data from ~2400 stations  Hourly precipitation analysis on a 0.25 o over the China  Currently hourly analyses constructed for 3-year period from 2005 to 2007  Interpolated through the optimal interpolation (OI) algorithm developed 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 climatology  Correction for the orographic effects through employment of the PRISM climatology

Sample hourly analysis  Sample 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 Satellite Estimates Using the Gauge Analysis

 Verified 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 / 0.25 o ×0.25 o

 Seasonal Mean Precipitation in Spring (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 (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 (3-hourly for Spring)  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 (3-hourly for Summer)  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 (3-hourly for Spring)  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 (3-hourly for Summer)  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 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 observation and satellite estimates in different seasons Satellite Products All monthsSpringSummer Bias(%)CorrBias(%)CorrBias(%)Corr CMORP H PERSIA NN COMB NRL B B42RT

 PDF of [3-Hourly] 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

Operational System of Daily precipitation analysis

 Flow Chart of this system Retrieve daily Observations Quality Control Climatology field Calculate Ratio Ratio analysis field Precipitation analysis: = × = × Service

 Data format: GrADS/ ArcGIS/GIF  This analysis includes the precipitation rate and gauge number distribution 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 users  CDC website :  Data format : GrADS ， ArcGIS and Gif  Data search way : format + time  Temporal/spatial resolution : daily/0.25deg

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From August 1 to September 8, 2008

CONCLUSIONS 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.

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