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