# Presentation: 470546

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

- Mingyue Chen
- &
- CPC Precipitation Working Group
- CPC/NCEP/NOAA

**The 32****th**** Annual Climate Diagnostics & Prediction Workshop**
**Oct. 22-26, 2007, Tallahassee, FL**

## Background

**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);*
**A project is under way at CPC**
- 		*to generate a unified precipitation analyses with improved quality and **	**consistent quantity;*
**The first step of the project **
- 		*is to construct a unified analysis of gauge-based daily precipitation over **	**global land;*
**To do this, we have to **
*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

**Assess the performance of three widely used gauge interpolation techniques and pick up the one with the best statistics**
**The three gauge interpolation algorithms:**
**Cressman (1959) **
**Distance weighting **
**Used to generate current regional analyses over US-Mexico and S. America**
**Shepard (1968)**
**Distance weighting**
**Used to generate GTS-based daily analysis over global land**
**Optimal Interpolation (OI) of Gandin (1965)**
**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 |  | OI |  |
| --- | --- | --- | --- | --- | --- | --- |
|  |           Corr. | Bias (%) |           Corr. | Bias (%) | 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**

**10% Network**

**1% Network**

-    Cressman spreads raining       	area substantially with 	sparse networks
-     OI reproduces the PDF 	very well

## 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

-    Comparison with CPC 	existing regional 	analysis over US for 	January 8, 1998;
-     Existing analysis is 	created using the 	Cressman method;
-    The new analysis 	presents finer 	structure in better 	agreements with 	station data;

-           Existing

-           New

## 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; 110W-	120W];
-    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

**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.**