A Global Daily Gauge-based Precipitation Analysis, Part I: Assessing Objective Techniques Mingyue Chen

A Global Daily Gauge-based
Precipitation Analysis, Part I:
Assessing Objective Techniques
Mingyue Chen
&
CPC Precipitation Working Group
CPC/NCEP/NOAA
The 32th Annual Climate Diagnostics & Prediction Workshop
Oct. 22-26, 2007, Tallahassee, FL
Background
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Problems with current CPC precipitation analyses
Due to historical reasons, the current precipitation analyses at CPC do not
take advantage of all available gauge and satellite data and present
inconsistencies among various products (e.g. global analysis does not
match with regional analyses);
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A project is under way at CPC
to generate a unified precipitation analyses with improved quality and
consistent quantity;
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The first step of the project
is to construct a unified analysis of gauge-based daily precipitation over
global land;
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To do this, we have to
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To unify daily gauge observation reports available at various places of CPC; and
To select an objective analysis technique to define the gauge-based analyses of
daily precipitation;
To produce the analysis for an extended period and on a real-time basis;
Objective of this talk
is to report the algorithm selection and data construction that we have
done;
Strategy of Algorithm Selection
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Assess the performance of three widely used gauge
interpolation techniques and pick up the one with the
best statistics
The three gauge interpolation algorithms:
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–
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Cressman (1959)
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Distance weighting
Used to generate current regional analyses over US-Mexico and S.
America
Shepard (1968)
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Distance weighting
Used to generate GTS-based daily analysis over global land
Optimal Interpolation (OI) of Gandin (1965)
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Implementation of Xie et al. (2007)
Interpolation of ratio of daily total to daily clim with orographic
adjustments
Used to generate regional gauge analysis over East Asia
Unified Daily Gauge Data
 Dense gauge networks from special CPC collections
over US, Mexico, and S. America;
 GTS gauge network elsewhere
 Daily reports available from ~17,000 stations
Daily Prcp Analyses for Jan.5, 2005
Precipitation Analyses:
Similar patterns, larger raining areas and smoother
distributions in Cressman
Correlation between Daily Analyses
 Calculated for 2005;
 Very high correlation
between OI and
Shepard;
 Less desirable correlation
between Cressman and
other analyses over
areas covered by less
gauges;
Cross-Validation Tests
 Withdraw daily precipitation reports at 10% stations
selected randomly;
 Define the analyzed values of precipitation at the
10% withdrawn stations by interpolating gauge
reports at the remaining 90% of the stations;
 Repeat this process for 10 times so that each station
is withdrawn once;
 Compare the analyzed values with the withdrawn
station observations to assess the performance
of the algorithms
Performance for Different Regions
 Cross-Validation Tests Results for 2005
 OI presents the highest correlation and small bias
over most regions
Cressman
Shepard
Bias (%)
OI
Corr.
Bias (%)
Corr.
Corr.
Bias (%)
Global
0.706
0.251
0.709
-0.085
0.735
-0.349
U.S.
0.793
0.754
0.784
-0.118
0.811
-0.467
Africa
0.364
3.316
0.354
1.259
0.377
-0.778
Histograms of Rainfall Intensity
 All three sets of analyses present lower frequencies for no-rain
strong rainfall events compared to gauge observations
 Cressman yields substantially reduced / inflated frequencies
for no-rain / light rain events
Gauge Network Density Impacts Tests
 To examine how the objective techniques perform in
interpolating station reports from gauge networks of
different densities;
 Select the CONUS as our test region for the
availability of a very dense;
 Create gauge-based analysis of daily precipitation
with a subset of all available gauge data and compare
the analysis with original station data
Correlation & Bias
 Quality of the gauge
analysis degrades as
gauge network density
becomes sparse
 OI performs the best with
the highest correlation
and the smallest biases
in most cases
Histograms of Rainfall Intensity
100% Network
 Cressman spreads raining
area substantially with
sparse networks

10% Network
OI reproduces the PDF
very well
1% Network
Construction of the Historical Analysis
 OI is selected to construct the gauge-based daily precipitation analysis
over the global land areas
 Quality control performed for the daily station reports;
 Historical analysis created for a 28-year period from 1979 to 2006;
 Time series of numbers of gauge reports available
Example for January 8, 1998
 Structure of precipitation
well depicted over
various parts of the
global land areas
Comparison with Existing Analysis
Existing
 Comparison with CPC
existing regional
analysis over US for
January 8, 1998;
New

Existing analysis is
created using the
Cressman method;
 The new analysis
presents finer
structure in better
agreements with
station data;
Comparison with Existing Analysis
 Comparison with CPC existing regional analysis over
S. America for January 8, 1998;
Existing
New
Station
Comparison of Area Mean Prcp
 Time series of mean
precipitation over NW
US [35N-45N; 110W120W];
 Green: Existing analysis
Red: New analysis
 Close agreements in time
series;
 New analysis shows
slightly larger
precipitation due to
improved analysis
method
Existing
New
Summary
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Performance of three widely used gauge interpolation methods has
been examined through cross-validation tests and gauge network
density impact tests.
Based on the assessments, the Optimal Interpolation (OI) algorithm
is selected to define our gauge-based analysis of daily
precipitation.
Quality control (QC) is performed for daily gauge precipitation
reports.
Gauge-based analyses have been created by interpolating the
QCed station data using the OI algorithm for an extended period
from 1979 to 2006.
Preliminary comparisons showed improved quality of our new
analysis compared to existing CPC analyses.
Further work is underway to check the new analysis and to apply it
for various climate studies.