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

projectproject

Jon GottschalckJon Gottschalck

University of Maryland, Baltimore County (UMBC)University of Maryland, Baltimore County (UMBC)Goddard Earth Science and Technology Center Goddard Earth Science and Technology Center

(GEST) Hydrological Sciences Branch(GEST) Hydrological Sciences BranchNASA / Goddard Space Flight CenterNASA / Goddard Space Flight Center

July 13, 2004July 13, 2004

Background – GLDAS Background – GLDAS Land Information Land Information System (LIS)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 Background – Land Information System (LIS) – cont.(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 precipitationprecipitation)

Methodology – General ProcedureMethodology – 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

Dataset Type Resolution Domain Source HIGGINS Gauge 0.25°x0.25°, daily CONUS NOAA / CPC GEOS Model 1.0°x1.25°, 3 hourly 90S-90N, 180W-180E NASA / GSFC GDAS Model ~0.4°, 6 hourly 90S-90N, 180W-180E NOAA / NCEP ECMWF Model ~0.2°, 3 hourly 90S-90N, 180W-180E ECMWF HUFFMAN Satellite 0.25°x0.25°, 3 hourly 60S-60N, 180W-180E NASA / GSFC PERSIANN Satellite 0.25°x0.25°, hourly 60S-60N, 180W-180E Univ. of Arizona CMORPH Satellite 8 km, half hourly 60S-60N, 180W-180E NOAA / CPC CMAP Merged ~0.4°, 6 hourly 90S-90N, 180W-180E NOAA / CPC AGRMET Merged 0.5°x0.5°, 3 hourly 90S-90N, 180W-180E AFWA NEXRAD Radar 4 km, hourly CONUS NOAA / NCEP

Methodology – Dataset SpecificationsMethodology – Dataset Specifications

Methodology – AssessmentMethodology – 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-CONUS - Seasonal Total Precipitation – March-May 2002May 2002

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

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

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

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

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

CONUS - Seasonal Total Precipitation – Dec. CONUS - Seasonal Total Precipitation – Dec. 2002-Feb. 20032002-Feb. 2003

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

CONUS SummaryCONUS Summary CONUS Root Mean Square Error CONUS Correlation

Dataset MAM JJA SON DJF MAM JJA SON DJF

GEOS 93.9 244.8 87.6 114.8 0.62 0.41 0.65 0.76 GDAS 95.0 167.8 77.6 66.4 0.72 0.53 0.53 0.82

ECMWF 69.1 107.6 65.6 55.8 0.66 0.41 0.68 0.80

Persiann 132.3 188.8 90.4 130.9 0.51 0.56 0.51 0.40 Huffman 293.5 221.8 93.6 285.8 0.47 0.54 0.52 0.36

CMORPH --------- ---------- ---------- 139.9 ---------- ---------- ---------- 0.43

CMAP 68.8 75.1 60.3 90.4 0.72 0.55 0.78 0.82 AGRMET 93.2 70.1 64.9 110.4 0.75 0.64 0.68 0.65

MEAN 73.3 110.9 46.3 79.3 --------- ---------- ---------- --------- NEXRAD 73.7 66.6 72.6 100.6 0.79 0.77 0.85 0.78

Evaluation of diurnal cycleEvaluation of diurnal cycle

Hourly composites of accumulation and frequency of precipitation

Calculated precipitation rate distribution

Eight locations:

Miami, FloridaMiami, Florida New Orleans, Louisiana Oklahoma City, Oklahoma Minneapolis, MinnesotaMinneapolis, Minnesota Phoenix, ArizonaPhoenix, Arizona Seattle, WashingtonSeattle, Washington Richmond, Virginia Boston, Massachusetts

JJA 2002 Diurnal Precipitation – Total JJA 2002 Diurnal Precipitation – Total PrecipitationPrecipitation

JJA 2002 Diurnal Precipitation – FrequencyJJA 2002 Diurnal Precipitation – Frequency

JJA 2002 Rate Distribution – FrequencyJJA 2002 Rate Distribution – Frequency

JJA 2002 Diurnal Precipitation – Total JJA 2002 Diurnal Precipitation – Total PrecipitationPrecipitation

JJA 2002 Diurnal Precipitation – FrequencyJJA 2002 Diurnal Precipitation – Frequency

JJA 2002 Diurnal Precipitation – Total JJA 2002 Diurnal Precipitation – Total PrecipitationPrecipitation

JJA 2002 Diurnal Precipitation – FrequencyJJA 2002 Diurnal Precipitation – Frequency

Assessment SummaryAssessment 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

Evaluation of diurnal cycle:

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.Assessment Summary – cont.

Upcoming plans for LIS

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 PlansUpcoming Plans

5-6 May 2003 Case study5-6 May 2003 Case study

(h)

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

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

Australia SummaryAustralia Summary