Validation of Reanalysis Daily Precipitation over the Americas

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NOS GIS Team NOS GIS Team

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Validation of Reanalysis

Daily Precipitation over the Americas

Viviane Silva, Vernon Kousky, Wayne Higgins and Emily Becker

CTB Seminar Series CTB Seminar Series

9 March 2009

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Motivation

• The Climate Prediction Center (CPC) plans to replace the current generation of reanalysis products in use for operational monitoring and prediction activities with a new generation of reanalysis products currently under development at the NCEP/ EMC.

• Before CPC can confidently base its operational climate monitoring and prediction activities on a new

generation of reanalysis and reforecast products, a

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Background - 1

• Since the mid-1990’s the CPC has used the

NCEP/NCAR reanalysis products and their real-time extension forward in time via the Climate Data

Assimilation System (CDAS) for operational climate monitoring and prediction activities.

• The current generation of reanalysis products is

among the most popular and widely used climate

data sets currently in existence.

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Background - 2

• The NCEP is currently developing the next generation of reanalysis products as part of the Climate Forecast System Reanalysis and Reforecast (CFSRR) project.

• The project is driven by NCEP’s

intraseasonal-to-interannual prediction

needs.

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EMC’s Plans

• The Environmental Modeling Center (EMC) plans call for the CFSRR to extend over the

period 1981-present.

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CPC’s Plans

• The CPC plans call for :

- the extension of the CFS reanalysis backward in time to 1948 and

- forward in real-time in order to satisfy

CPC’s operational climate monitoring and

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Advantages of the CFS Reanalysis Extension Backward in Time

• Will increase the number of cases of the low

frequency modes of climate variability, such

as ENSO, for a proper comparison of the CFSR

to the current generation of reanalysis.

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NOTE

• We do not expect seasonal re-forecasts out to 8 months, during 1948-1980, to be as

skillful as those for the period 1981- present, because the ocean states

generated prior to 1981 are not as reliable.

• Not enough ocean observations.

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What we have done

so far …

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NCEP/NCAR Reanalysis (R1) &

NCEP/DOE Reanalysis (R2)

• We documented the biases in R1 via comparison to

observations (gridded analysis) for US and South America.

• Current Focus: Quality of R1 daily precipitation statistics.

• The NCEP/DOE Reanalysis (R2), obtained using an updated forecast model and data assimilation system, is also used at CPC, so we evaluated both R1 and R2.

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Three Basic Statistics

(1979-2006)

• Difference in Means

• Ratio of Variances

• Temporal Correlation

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Preliminary Results

United States

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Mean Daily Precipitation Difference

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Probability P ≥ 1 mm

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Annual cycle of the probability of daily precipitation ≥ 1 mm for selected locations in the Southeast

Additional Details:

• R1 and R2 probabilities are:

1. greater than observed during the warm season (especially June to

September), with greater biases in R1,

2. and smaller than

observed during the cold season (especially

November to February), with greater biases in R2.

• The largest biases are during the warm season in R1 (and to a lesser extent in R2).

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Probability P ≥ 10 mm

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Annual cycle of the probability of daily precipitation ≥ 10 mm for selected locations in the Southeast

• The observed probabilities remain relatively constant through the annual cycle (including the warm season).

• while the reanalysis

probabilities show a distinct maximum during the warm season as they did for prob. >

1mm.

• Again, this is an indication that the overestimates in R1 and R2 are for the relatively heavy (convective)

precipitation events.

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Ratio of Variance of Daily Precipitation

a) R1/OBS b) R2/OBS

• During OCT-APR, both R1 and R2 exhibit less variability than OBS on daily timescales over eastern TX, the Gulf Coast states and Tennessee Valley.

• The reanalyses display more variability than observed over the West throughout the year (exception along

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Ratio of Variance of Daily Precipitation

c) R2/R1

• R2 variance is greater than R1 variance across the entire CONUS during May-October (greatest

differences during July-September).

• In addition, R2 variance exceeds R1 variance along the Gulf Coast during November-April.

• Since the R2 and R1 variances are less than OBS during November-April

(previous slide), the R2/R1 ratio is >1 shown in this slide indicates that R2 is closer to OBS in the Gulf region.

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Temporal Correlation (x100)

• High correlation in winter and lower

correlation in summer when convection is dominant.

• Throughout the year, the correlations

between R2 and OBS are lower than those for R1 and OBS.

• So, in spite of

improvements in the mean bias of

daily precipitation in R2 (shown before), the

R1 & OBS R2 & OBS

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Mean Daily Precipitation (mm) during El Niño

Expressed as departures from the appropriate period mean.

OBS R1 R2

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Mean Daily Precipitation (mm) during La Niña

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Annual cycle of the probability of daily precipitation for selected locations in the Southeast - El Niño

• El Niño-related

probability anomalies are too large in the Reanalyses, especially during April-September, with R1 larger than R2, except for region 6 (Florida).

• Similar results are obtained for daily

precipitation greater than 10 mm.

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Annual cycle of the probability of daily precipitation for selected locations in the Southeast - La Niña

During La Niña, the probability anomalies are again larger in R1 than R2 during the

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Preliminary Results

South America

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Probability P> 1mm

Difference: R1- OBS

Difference: R2 - OBS

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Probability P> 5mm

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Annual cycle of the probability of daily precipitation ≥ 1 mm for selected locations in South America

1 3

2

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Expected Outcome

• A careful validation of the current generation of reanalysis products will provide a benchmark for

determining improvements in the new generation of reanalysis (CFSR) products.

• Results from this study will be used:

– to investigate the frequency distribution of the CFS daily precipitation forecasts and

– to identify and correct biases in the statistics of daily

precipitation within a season to improve CPC operational monitoring and climate forecast products.

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Future Work - 1

• Once the CFS Reanalysis is available, the associated

precipitation statistics will be compared to those presented here.

• we also plan comparisons of the circulation features, especially for fields used in CPC’s suite of real-time monitoring products.

• Comparisons of circulation features will be done to shed light on the degree of uncertainty in the analyses, rather

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• To investigate the dependence of daily precipitation statistics in R1, R2, CFS-R and observations on major climate modes, including ENSO, MJO and AO, and on teleconnection patterns such as the Pacific-North American pattern.

• One focus will be on the behavior of the precipitation frequency distributions during developing or decaying phases of the major climate modes.

Future Work - 2

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Questions ? Suggestions ?

Viviane.Silva@noaa.gov

Modeling

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