# Presentation: 376454

## Analysis of multiple precipitation products as part of the Global Land Data Assimilation System (GLDAS) project

**Jon Gottschalck**
***University of Maryland, Baltimore County (UMBC)***
***Goddard Earth Science and Technology Center (GEST) Hydrological Sciences Branch***
***NASA / Goddard Space Flight Center***
**July 13, 2004**

## Background – GLDAS  Land Information System (LIS)

-  Merging of GSFC NLDAS and GLDAS codes
-  Offline global high resolution terrestrial modeling system
-  Multiple resolutions (2.0° x 2.5°, 1.0°, 1/2°, 1/4°, 1/8°, 5 km, 1 km)
-  Capability of running over regional domains (*e.g*., CONUS)
-  Runs 4 LSMs: Mosaic, Noah, CLM2, and VIC
-  Baseline atmospheric forcing from GDAS, GEOS, ECMWF

## Background – Land Information System (LIS) – cont.

-  UMD vegetation classification (AVHRR, MODIS), “tiling approach”
-  High resolution soil data (Reynolds *et al*. 2000)
-  Lookup table and satellite based LAI (AVHRR, MODIS)
-  Meteorological forcing corrected for elevation (*P*, *T*, *LW*, and *q*)
-  Satellite based observations update critical forcing fields (SW/LW radiation and _***precipitation***_)

## Methodology – General Procedure

* *_*Purpose*_: Obtain an understanding of the accuracy and usefulness of a 	      number of precipitation estimates in order to determine the 	      best way to proceed for LIS precipitation forcing
* *_*Initial analysis period*_:   March 2002 – February 2003
- 		                    (currently extending through February 2004)
-  _*Regions of*__ __*Analysis*_: CONUS, Australia
-  _*Types of*__ __*Datasets*_:
-  _Global modeling system_ estimates: ***GEOS, GDAS, ECMWF***
-  _Satellite only derived_ estimates: ***Persiann, Huffman***, CMORPH
-  _Merged satellite and gauge_ estimates: ***CMAP***, AGRMET
-  _Ground radar_ estimates: ***Stage II NEXRAD***
-  _Gauge only_ estimates: ***Higgins******, ***Ebert

## Methodology – Dataset Specifications

_**Methodology – Dataset Specifications**_

## Methodology – Assessment

* *Approach focuses on “end user” concept
* *_*Methods of Assessment*_:
-  Seasonal accumulation
-  Seasonal correlation of daily precipitation
-  Evaluation of warm season diurnal cycle
- 	  accumulation and frequency
-  Distribution of warm season precipitation rate

## CONUS - Seasonal Total Precipitation – March-May 2002

## CONUS - Correlation of Daily Precipitation – March-May 2002

## CONUS - Seasonal Total Precipitation – June-August 2002

## CONUS - Correlation of Daily Precipitation – June-August 2002

## CONUS - Seasonal Total Precipitation – Sept.-Nov. 2002

## CONUS - Correlation of Daily Precipitation – Sept.-Nov. 2002

## CONUS - Seasonal Total Precipitation – Dec. 2002-Feb. 2003

## CONUS - Correlation of Daily Precipitation – Dec.-Feb. 2003

## CONUS Summary

## Evaluation of diurnal cycle

-  Hourly composites of accumulation and frequency of precipitation
-  Calculated precipitation rate distribution
-  Eight locations:
-  ***Miami, Florida***
-  New Orleans, Louisiana
-  Oklahoma City, Oklahoma
-  ***Minneapolis, Minnesota***
-  ***Phoenix, Arizona***
-  ***Seattle, Washington***
-  Richmond, Virginia
-  Boston, Massachusetts

## JJA 2002 Diurnal Precipitation – Total Precipitation

## JJA 2002 Diurnal Precipitation – Frequency

## JJA 2002 Rate Distribution – Frequency

## JJA 2002 Diurnal Precipitation – Total Precipitation

## JJA 2002 Diurnal Precipitation – Frequency

## JJA 2002 Diurnal Precipitation – Total Precipitation

## JJA 2002 Diurnal Precipitation – Frequency

## Assessment Summary

*** ***_***Seasonal total precipitation***_:
-  **CMAP** has lowest error in spring, summer, and fall
-  **ECMWF** performs the best of the model estimates
-  _***Correlation of daily precipitation***_**:**
-  **CMAP** and **AGRMET** show the greatest correlation overall
-  **GDAS** and **ECMWF** perform the best of the model products
-  **Persiann** and **Huffman** show good correlation during summer especially over the central US

## Assessment Summary – cont.

** **Currently, inconclusive results for accumulation
-  Persiann performs well in Miami, FL
-  CMAP / AGRMET perform well in Minneapolis, MN
-  Satellite products overestimate in Phoenix, AZ
-  **Persiann**, **Huffman**, and **AGRMET** are best for frequency

_**Assessment Summary – cont.**_

## Upcoming Plans

-  Based on seasonal totals and correlation plan to use** CMAP**
-  Alter **CMAP** temporal disaggregation; Investigate using **Persiann**, **AGRMET**, or **Huffman** to interpolate **CMAP**
** **Extend analysis period into 2004 and evaluate Australia

_**Upcoming Plans**_

## Slide 27

## 5-6 May 2003 Case study

## Slide 29

## Australia - Seasonal Total Precipitation – Dec.-Feb. 2003

## Australia - Seasonal Total Precipitation – June-August 2002

## Australia Summary

_**Australia Summary**_